# -*- coding: utf-8 -*-
# Prompts
bg_system_prompts = {
    "English": """You are an expert in the field of computer science, especially familiar with artificial intelligence. Use a academic style to answer the following questions.""",
    "Chinese": """你是一位计算机科学领域的专家，尤其熟悉人工智能。请使用学术语言风格回答以下问题。"""}
json_system_prompts_basic = {"English": """You should always follow the instructions and output a valid JSON object. Please use the specified JSON object structure according to the instructions.
If you are unsure, use {"answer": "$your_answer"} by default. Make sure to always end the code block with "```" to indicate the end of the JSON object.""",
                             "Chinese": """您应该始终遵循指令并输出一个有效的JSON对象。请根据指令使用指定的JSON对象结构。
                             如果不确定，请默认使用 {"answer": "$your_answer"}。确保始终以 "```" 结束代码块，以指示JSON对象的结束。"""}
json_format_prompts = {"English": """**Output format**
Please strictly follow the following format to output only JSON, do not output Python code or other information, JSON fields are separated by the tonnage [,]:\n""",
                       "Chinese": """### 输出格式
                       请严格按照如下格式仅输出JSON，不要输出Python代码或其他信息，JSON字段使用顿号【、】区隔：\n"""}

judge_relevance_prompt = """You are a relevance assessment expert. Based on the following information, determine whether the article is relevant to the specified topic. Please respond with only "Yes" or "No".
 
   Article Title: \"%s\"
   Article Abstract: \"\"\"%s\"\"\"
   Specified Topic: \"%s\"
 
   Your response (Yes/No):"""
judge_relevance_by_short_text_prompt = """You are a relevance assessment expert. Based on the following information, determine whether the entity is relevant to the section title. Please respond with only "Yes" or "No".

   Entity Name: \"%s\"
   Entity Description: \"%s\"
   Section Title: \"%s\"

   Your response (Yes/No):"""
extract_lang_kw_prompt = """
**指令**
请判断用户期望大模型作答时所使用的语种。若用户没有明确指出期望语种，则输出用户输入所使用的语种。

具体输出规则如下：

若期望语种是英语，则输出\"English\"。
若期望语种是中文，则输出\"Chinese\"。
若期望语种是其他语种，则直接输出该语种的名称。

这是用户输入，请提供输出结果。
**用户输入**
\"\"\"%s\"\"\"

仅输出最终判断结果，直接回答某一个特定语种，切勿展示任何分析过程。
"""
extract_type_prompt = """
**指令**
请根据以下分类标准，将用户的综述写作需求归类至相应的四大类别：
a. 概述某一技术概念；
b. 综述特定研究方向的研究现状；
c. 对多种方法进行对比分析与汇总；
d. 探究某一技术方法的发展历程。

**示例**
\"\"\"
%s
\"\"\"

以下为用户的具体需求，请根据内容将其归类至合适的类别。
**用户需求**
\"\"\"
%s
\"\"\"
请直接输出您的分类结果，无需附上分析过程。您的输出应准确对应以下类别之一：{\"技术概念\", \"方向研究现状\", \"方法对比分析\", \"技术方法发展历程\"}。"""
extract_topic_prompts = [
    {"Chinese": """**指令**
作为计算机领域的研究助手，你的任务是精准识别用户输入中的核心技术概念。请严格依据用户提供的句子，准确提炼出综述所需针对的具体技术概念。确保不添加任何主观臆断或未提及的信息，保持客观和精准。

**示例**
%s

**用户输入句子**
%s

请直接输出该技术概念的名称，无需展示分析过程。""",
     "English": """**Instruction**
As a research assistant in the field of computer science, your task is to precisely identify the core technical concept from the user's input. Strictly based on the provided sentence, accurately extract the specific technical concept for the review. Ensure that no subjective assumptions or unmentioned information are added.

**Example**
%s

**User Input Sentence**
%s

Please directly output the name of the technical concept without displaying the analysis process."""},
    {"Chinese": """**指令**
作为计算机领域的专业研究助手，你的核心任务是精准识别用户输入中的具体研究方向。请严格依据用户提供的句子，准确提炼出综述所需针对的特定研究方向。确保过程中不添加任何主观臆断或未提及的信息，保持客观和精准。

**示例**
%s

**用户输入句子**
%s

请直接输出该研究方向的名称，无需展示分析过程。""",
     "English": """**Instruction**
As a professional research assistant in the field of computer science, your core task is to precisely identify the specific research direction from the user's input. Strictly based on the provided sentence, accurately extract the particular research direction for the review. Ensure that no subjective assumptions or unmentioned information are added, maintaining objectivity and precision.

**Example**
%s

**User Input Sentence**
%s

Please directly output the name of the research direction without displaying the analysis process."""},
    {"Chinese": """**指令**
作为计算机领域的专业研究助手，你的核心任务是精准识别用户输入中的具体问题。请严格依据用户提供的句子，准确提炼出综述所需针对的特定问题，确保过程中不添加任何主观臆断或未提及的信息，保持客观和精准。

**示例**
%s

**用户输入句子**
%s

请直接输出该问题的名称，无需展示分析过程。""",
     "English": """**Instruction**
As a professional research assistant in the field of computer science, your core task is to precisely identify the specific problem from the user's input. Strictly based on the provided sentence, accurately extract the particular problem for the review, ensuring that no subjective assumptions or unmentioned information are added, maintaining objectivity and precision.

**Example**
%s

**User Input Sentence**
%s

Please directly output the name of the problem without displaying the analysis process."""},
    {"Chinese": """**指令**
作为计算机领域的专业研究助手，你的核心任务是精准识别用户输入中的具体技术方法。请严格依据用户提供的句子，准确提炼出综述所需针对的特定技术方法，确保过程中不添加任何主观臆断或未提及的信息，保持客观和精准。

**示例**
%s

**用户输入句子**

%s

请直接输出该技术方法的名称，无需展示分析过程。""",
     "English": """**Instruction**
As a professional research assistant in the field of computer science, your core task is to precisely identify the specific technical method from the user's input. Strictly based on the provided sentence, accurately extract the particular technical method for the review, ensuring that no subjective assumptions or unmentioned information are added, maintaining objectivity and precision.

**Example**
%s

**User Input Sentence**
%s

Please directly output the name of the technical method without displaying the analysis process."""}]

extract_topic_samples = [
    {"Chinese": """
     Input: \"我想了解更多关于深度学习中的损失函数，你能帮我做个综述吗？\", Output: \"深度学习中的损失函数\"
     Input: \"我想知道什么是大语言模型的微调，你能帮我做个综述吗？\", Output: \"大语言模型的微调\"
     Input: \"什么是深度主动学习\", Output: \"深度主动学习\"""",
     "English": """
     Input: \"I want to know more about loss functions in deep learning. Can you provide me with a review?\", Output: \"Loss functions in deep learning\"
     Input: \"I want to know what fine - tuning of large language models is. Can you provide me with a review?\", Output: \"Fine - tuning of large language models\"
     Input: \"What is deep active learning?\", Output: \"Deep active learning\""""},
    {"Chinese": """
     Input: \"我听说Text2SQL最近挺火的，你能告诉我它目前的研究进展和面临的挑战吗？\", Output: \"Text2SQL\"
     Input: \"隐式篇章关系识别任务研究现状如何？\", Output: \"隐式篇章关系识别\"
     Input: \"知识增强的文本生成研究进展如何？\", Output: \"知识增强的文本生成\"""",
     "English": """
     Input: \"I heard that Text2SQL has been very popular recently. Can you tell me about its current research progress and challenges?\", Output: \"Text2SQL\"
     Input: \"What is the current research status of implicit discourse relation recognition tasks?\", Output: \"Implicit discourse relation recognition\"
     Input: \"What is the research progress of knowledge-enhanced text generation?\", Output: \"Knowledge-enhanced text generation\""""},
    {"Chinese": """
     Input: \"我想提升我的大模型的规划能力，你能帮我比较一下有哪些方法吗，它们各自的优缺点是什么？\", Output: \"提升大模型的规划能力\"
     Input: \"有哪些方法可以缓解大模型的幻觉问题，各自优劣是什么？\", Output: \"缓解大模型的幻觉问题\"
     Input: \"如何优化大模型的训练效率，不同优化策略的特点和适用场景是怎样的？\", Output: \"优化大模型的训练效率\"""",
     "English": """
     Input: \"I want to improve the planning ability of my large model. Can you help me compare what methods are available and what are their respective advantages and disadvantages?\", Output: \"Improve the planning ability of large models\"
     Input: \"What methods can be used to alleviate the hallucination problem of large models and what are their respective advantages and disadvantages?\", Output: \"Alleviate the hallucination problem of large models\"
     Input: \"How to optimize the training efficiency of large models and what are the characteristics and applicable scenarios of different optimization strategies?\", Output: \"Optimize the training efficiency of large models\""""},
    {"Chinese": """
     Input: \"我对多模态大模型的发展很感兴趣，你能给我讲讲它的技术发展路线吗？\", Output: \"多模态大模型\"
     Input: \"讲讲表示学习技术的发展路线\", Output: \"表示学习\"
     Input: \"大语言模型的技术发展路线是什么样的？\", Output: \"大语言模型\"""",
     "English": """
     Input: \"I'm very interested in the development of multimodal large models. Can you tell me about its technological development route?\", Output: \"Multimodal large models\"
     Input: \"Tell me about the development route of representation learning technology\", Output: \"Representation learning\"
     Input: \"What does the technological development route of large language models look like?\", Output: \"Large language models\""""}]

extract_entity_prompt = """
**Instruction** 
You are an AI specialized in extracting computer science academic entities from text. Your goal is to identify and return the specific terms that are representative of computer science concepts, while avoiding general category names.
For each input text, carefully analyze and extract the following:
- Computer science academic entities that are specific and convey a particular task, method, method, technology or concept.
- Exclude any terms that are too broad, such as general categories or words that do not specifically indicate a unique concept within the field.
- Refrain from splitting indivisible academic tasks or concepts into overly general components.
Here are the guidelines for extraction:
Extract: 'Deep Learning Technology' (specific)
Avoid: 'Technology' (general)
Extract: 'hallucination of LLM' (specific concept related to LLM)
Avoid: 'limitation of LLM' (broader and not a specific concept)
Extract: 'Implicit Discourse Relation Recognition'(complete and indivisible)
Avoid: ['Implicit', 'discourse relation', 'Recognition'](separated)
**Examples**
\"\"\"
%s
\"\"\"
extract the academic one or two entity from the following text:
\"\"\"%s\"\"\"
"""
optimize_acad_query_prompt = """
**Instruction**
You are designed to optimize literature search queries. For each input provided, analyze the academic entity and provide the following output:
- If and only if the term is a general vocabulary, output the string: \"None\".
Examples:
Input: \"Technical Development Route\", Output: \"None\",
Input: \"Limitation\", Output: \"None\",

- If the term is commonly used in literature titles, output it directly.
Examples:
Input: \"Multimodal Large Model\", Output:\"Multimodal Large Model\",
Input: \"Large Language Model\", Output\"Large Language Model\",
Input: \"Fine-tuning\", Output:\"Fine-tuning\",
Input: \"Text2SQL\", Output: \"Text2SQL\",

- If the term is not common in literatures, remove and only remove the general part of the query and output it.
Input: \"Loss Function\", Output: \"Loss\",
Input: \"Question Answering System\", Output:\"Question Answering\",
Input: \"Deep Learning Technology\", Output:\"Deep Learning\",
Input: \"Recommendation System\", Output:\"Recommendation\",

Here is the input:
\"\"\"%s\"\"\"
please provide the output and the explanation
"""
extract_keyinfo_prompts = [
    {"Chinese": {
        "非核心内容": """你的任务是从一篇涉及到\"技术概念\"的论文文献中提取关于技术概念及其\"预备知识\"、\"应用情况\"和\"挑战与局限性\"的关键信息，并按照以下结构进行整理：
            **结构**
            1. 引言（Introduction）

            1.1 背景（Background）

            请描述\"技术概念\"产生的背景，包括相关领域的发展状况和面临的问题。
            1.2 研究动机（Motivation）

            阐述研究\"技术概念\"的动机。强调其在解决实际问题、推动相关领域发展等方面的意义。
            1.3 研究目标（Research Objectives）

            明确研究的具体目标。
            例如：对\"技术概念\"进行全面梳理、分析其性能、探讨应用场景等。
            2. 概念与预备知识（Concepts and Preliminaries）

            2.1 \"技术概念\"的定义与解释（Definition and Explanation of the Technology Concept）

            详细解释\"技术概念\"的定义和基本原理解释。确保读者对其有清晰的理解。
            2.2 与其他相关技术的关系（Relationship with Other Related Technologies）

            对比分析\"技术概念\"与相关技术的区别和联系。
            2.3 关键组成部分与特征（Key Components and Characteristics）

            剖析该\"技术概念\"的关键组成部分。
            解释其核心特征。
            阐述这些要素如何影响技术的功能和性能。
            5. 应用（Applications）

            5.1 应用领域（Application Areas）

            概述\"技术概念\"在本文所涉及领域的应用情况。
            5.2 案例研究（Case Studies）

            详细分析本文所提到的应用案例。
            应包括：
            问题描述：简要描述案例中需要解决的具体问题。
            \"技术概念\"应用过程：详细说明\"技术概念\"是如何被应用于解决问题的。
            取得的效果：阐述应用\"技术概念\"后取得的实际效果和影响。

            6. 挑战与局限性（Challenges and Limitations）

            6.1 技术挑战（Technical Challenges）

            提取本文中提到的\"技术概念\"在发展过程中面临的技术难题。
            提取本文提到的其产生的原因及可能的解决方案。
            6.2 实践中的局限性（Limitations in Practice）

            讨论\"技术概念\"在实际应用中存在的局限性。
            例如，性能瓶颈、资源消耗、可扩展性等问题，以及对应用效果的影响。
            6.3 潜在研究方向（Potential Research Directions）

            根据参考文献展望\"技术概念\"未来的研究方向。
            例如，在理论研究、技术改进、应用拓展等方面的可能性，激发读者的研究兴趣。
            6.4 预期发展与趋势（Expected Developments and Trends）

            预测\"技术概念\"未来的发展趋势。
            例如，与其他技术的融合趋势、在新兴领域的应用前景等，为相关研究和实践提供参考。

            以下是给定的论文文献
            **论文文献**
            \"\"\"
            %s
            \"\"\"
            请确保提取的信息准确、全面，并按照上述结构进行呈现。""",
        "方法与技术路线": """你的任务是从一篇涉及到\"技术概念\"的论文文献中提取\"方法与技术路线\"部分的关键信息，结构化地呈现如下内容：
            3. 方法与技术路线（Methods and Approaches）
            3.1 方法（Methods）

            列举利用\"技术概念\"时用到的方法。
            请详细说明其基本原理、操作步骤及适用场景。
            3.2 先进方法（Advanced Approaches）

            介绍本文用到的先进方法或研究进展。
            分析这些先进方法相对于传统方法的优势。
            阐述其在解决特定问题上的创新性。
            3.3 方法的创新性与局限性（The Innovativeness and Limitations of the Method）

            分析该方法的创新性。
            指出该方法的局限性。

            以下是给定的论文文献
            **论文文献**
            \"\"\"
            %s
            \"\"\"
            请确保提取的信息准确、全面，并按照上述结构进行呈现。"""
    },
        "English": {
            "non-core content": """
    Your task is to extract key information regarding a \"an overview of a certain technological concept\", its \"prerequisite knowledge\", \"application scenarios\", and \"challenges and limitations\" from a research paper involving the \"an overview of a certain technological concept\", and organize it according to the following structure:

### Structure
1. **Introduction**
- **1.1 Background**
Describe the background of the emergence of the \"an overview of a certain technological concept\", including the development status and problems faced in the relevant field.
- **1.2 Motivation**
Elaborate on the motivation for researching the \"an overview of a certain technological concept\". Highlight its significance in solving practical problems and promoting the development of related fields.
- **1.3 Research Objectives**
Clarify the specific research objectives. For example, conduct a comprehensive review of the \"an overview of a certain technological concept\", analyze its performance, and explore application scenarios.
2. **Concepts and Preliminaries**
- **2.1 Definition and Explanation of the Technology Concept**
Explain in detail the definition and basic principle of the \"an overview of a certain technological concept\". Ensure that readers have a clear understanding.
- **2.2 Relationship with Other Related Technologies**
Compare and analyze the differences and connections between the \"an overview of a certain technological concept\" and related technologies.
- **2.3 Key Components and Characteristics**
Analyze the key components of the \"an overview of a certain technological concept\". Explain its core characteristics. Elaborate on how these elements affect the function and performance of the technology.
5. **Applications**
- **5.1 Application Areas**
Provide an overview of the application of the \"an overview of a certain technological concept\" in the fields covered in this paper.
- **5.2 Case Studies**
Analyze in detail the application cases mentioned in this paper. It should include:
    - **Problem Description**: Briefly describe the specific problem to be solved in the case.
    - **Application Process of the \"an overview of a certain technological concept\"**: Explain in detail how the \"an overview of a certain technological concept\" is applied to solve the problem.
    - **Achieved Results**: Elaborate on the actual results and impacts obtained after applying the \"an overview of a certain technological concept\".
6. **Challenges and Limitations**
- **6.1 Technical Challenges**
Extract the technical problems faced by the \"an overview of a certain technological concept\" during its development as mentioned in this paper. Extract the causes and possible solutions mentioned in this paper.
- **6.2 Limitations in Practice**
Discuss the limitations of the \"an overview of a certain technological concept\" in practical applications. For example, issues such as performance bottlenecks, resource consumption, scalability, and their impacts on application effects.
- **6.3 Potential Research Directions**
Based on the reference literature, look ahead to the future research directions of the \"technical concept\". For example, possibilities in theoretical research, technical improvement, application expansion, etc., to stimulate readers' research interests.
- **6.4 Expected Developments and Trends**
Predict the future development trends of the \"an overview of a certain technological concept\". For example, trends of integration with other technologies, application prospects in emerging fields, etc., to provide reference for relevant research and practice.

The following is the given research paper:

### Research Paper
\"\"\"
%s
\"\"\"

Please ensure that the extracted information is accurate and comprehensive, and presented according to the above structure.""",
            "method and technical route": """
Your task is to extract the key information from the \"Methods and Technical Routes\" section of a research paper involving the \"an overview of a certain technological concept\", and present the following content in a structured manner:

### 3. Methods and Approaches
#### 3.1 Methods
Enumerate the methods used when applying the \"an overview of a certain technological concept\". Provide a detailed description of their basic principles, operational steps, and applicable scenarios.

#### 3.2 Advanced Approaches
Introduce the advanced methods or research progress employed in this paper. Analyze the advantages of these advanced methods compared to traditional methods. Elaborate on their innovativeness in solving specific problems.

#### 3.3 The Innovativeness and Limitations of the Method
Analyze the innovativeness of this method. Point out the limitations of this method.

The following is the given research paper:

### Research Paper
\"\"\"
%s
\"\"\"

Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure."""}},
    {"Chinese": {
        "非核心内容": """你的任务是从一篇以\"方向研究现状\"为主题的论文文献中提取关于介绍该方向研究部分、\"相关工作\"、\"应用情况\"和\"挑战与未来方向\"部分的关键信息，并结构化地呈现如下内容：
        **结构**
        1. 引言

        1.1 方向研究背景

        阐述研究\"方向研究现状\"的意义和价值。
        说明\"方向研究现状\"对计算机领域，相关行业以及实际应用中的重要贡献。
        介绍\"方向研究现状\"的发展历程。
        描述\"方向研究现状\"中当前的研究热点。
        1.2 方向研究现状

        简述本篇论文关于\"方向研究现状\"的成果。
        基于本篇论文分析\"方向研究现状\"地研究趋势。
        根据本篇论文发现\"方向研究现状\"的研究空白。
        2. 相关工作

        2.1 早期研究

        基于本篇论文文献的内容回顾\"方向研究现状\"的早期重要研究成果，介绍这些成果对后续研究的奠基作用，及其提出的关键概念、理论或方法。
        2.2 近期进展

        总结本篇论文在\"方向研究现状\"上取得的主要研究进展，如重要的算法改进、模型创新、新的应用案例等。
        2.3 \"方向研究现状\"的分类与比较

        根据本篇论文提供的信息，对现有研究进行分类，分类标准可以是技术方法、应用领域、理论模型等。
        比较不同类别研究之间的异同点。
        分析各自的优势和局限性。
        5. 应用情况

        5.1 不同领域的应用案例

        列举\"方向研究现状\"研究成果在多个具体领域或下游任务中的应用实例。
        详细描述每个案例中如何运用相关技术解决实际问题。
        说明每个案例中取得的成果。
        5.2 应用效果与影响

        分析\"方向研究现状\"研究在各个应用领域中产生的效果和影响。
        包括对提高效率、优化性能、改善用户体验、推动行业发展等方面的贡献。
        提及在应用过程中遇到的挑战和问题。
        6. 挑战与未来方向

        6.1 当前面临的挑战

        依据本篇论文，列举在\"方向研究现状\"研究和应用过程中当前面临的主要挑战。
        如技术瓶颈、数据质量问题、算法复杂度、伦理道德问题等。
        探讨这些挑战对研究进展和实际应用的阻碍。
        6.2 未来发展趋势

        依据本篇论文，展望\"方向研究现状\"未来的发展趋势。
        预测可能出现的新技术、新方法、新应用领域或研究热点。
        结合当前的技术发展趋势和实际需求，提出对未来研究方向的建议。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。""",
        "研究方法": """你的任务是从一篇以\"方向研究现状\"为主题的论文文献中提取该论文中关于\"研究方法与技术\"和\"实验与评估\"部分的关键信息，并结构化地呈现如下内容：
        **结构**
        3. 研究方法与技术

        3.1 主要方法概述

        介绍本篇论文文献在该研究方向上的研究方法。
        包括其基本原理、核心步骤和适用场景。
        3.2 方法细节

        阐述本篇论文文献提出的方法的特点。
        描述方法的实现细节。
        说明该方法与其他方法的区别。
        3.3 方法的比较与评估

        对比本篇论文文献提出的方法在性能、效率、准确性、可扩展性等方面的表现。
        评估该方法在实际应用中的优缺点。
        可以通过实验结果、案例分析或理论推导等方式进行比较。
        4. 实验与评估

        4.1 数据集介绍

        介绍在\"方向研究现状\"研究中常用的实验数据集。
        包括数据集的来源、规模、特点以及适用范围。
        解释为何选择这些数据集进行实验研究。
        4.2 评估指标

        列举用于评估\"方向研究现状\"研究成果的主要评估指标。
        说明每个指标的含义以及在评估中的重要性。
        4.3 实验结果分析

        深入分析本篇论文给出的实验结果。
        解释结果背后的原因。
        探讨本篇论文方法对实验结果的影响。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。
        """
    },
        "English": {
            "non-core content": """
        Your task is to extract the key information from a research paper themed on \"the research status of a specific research direction\" regarding the sections introducing the research of this direction, \"Related Work\", \"Application Situations\", and \"Challenges and Future Directions\", and present it in a structured manner as follows:

**Structure**
1. Introduction
    - 1.1 Research Background
Elaborate on the significance and value of researching \"the research status of a specific research direction\".
Illustrate the important contributions of \"the research status of a specific research direction\" to the computer field, related industries, and practical applications.
Introduce the development history of \"the research status of a specific research direction\".
Describe the current research hotspots in \"the research status of a specific research direction\".
    - 1.2 The Current Research Status
Briefly describe the achievements of this paper regarding \"the research status of a specific research direction\".
Analyze the research trends of \"the research status of a specific research direction\" based on this paper.
Identify the research gaps of \"the research status of a specific research direction\" according to this paper.
2. Related Work
    - 2.1 Early Research
Based on the content of this research paper, review the early important research achievements of \"the research status of a specific research direction\", introduce the foundational role of these achievements in subsequent research, and the key concepts, theories, or methods they proposed.
    - 2.2 Recent Progress
Summarize the main research progress of this paper in \"the research status of a specific research direction\", such as important algorithm improvements, model innovations, new application cases, etc.
    - 2.3 Classification and Comparison of \"the research status of a specific research direction\"
According to the information provided by this paper, classify the existing research. The classification criteria can be technical methods, application fields, theoretical models, etc.
Compare the similarities and differences among different categories of research.
Analyze their respective advantages and limitations.
5. Application Situations
    - 5.1 Application Cases in Different Fields
List the application examples of the research achievements of \"the research status of a specific research direction\" in multiple specific fields or downstream tasks.
Describe in detail how the relevant technologies are applied to solve practical problems in each case.
Explain the achievements obtained in each case.
    - 5.2 Application Effects and Impacts
Analyze the effects and impacts of the research on \"the research status of a specific research direction\" in various application fields.
Including contributions to improving efficiency, optimizing performance, enhancing user experience, and promoting industry development.
Mention the challenges and problems encountered during the application process.
6. Challenges and Future Directions
    - 6.1 Current Challenges
Based on this paper, list the main challenges currently faced in the research and application process of \"the research status of a specific research direction\".
Such as technical bottlenecks, data quality issues, algorithm complexity, ethical and moral issues, etc.
Discuss the obstacles of these challenges to research progress and practical applications.
    - 6.2 Future Development Trends
Based on this paper, look ahead to the future development trends of \"the research status of a specific research direction\".
Predict the possible emergence of new technologies, new methods, new application fields, or research hotspots.
Combined with the current technological development trends and actual needs, put forward suggestions for future research directions.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure. """,
            "research methodology": """Your task is to extract the key information from the \"Research Methods and Technologies\" and \"Experiments and Evaluations\" sections of a research paper themed on \"the research status of a specific research direction\", and present it in a structured manner as follows:

**Structure**
3. Research Methods and Technologies
    - 3.1 Overview of Main Methods
Introduce the research methods in this research paper for this research direction. Include its basic principles, core steps, and applicable scenarios.
    - 3.2 Method Details
Elaborate on the characteristics of the methods proposed in this research paper. Describe the implementation details of the methods. Explain the differences between this method and other methods.
    - 3.3 Comparison and Evaluation of Methods
Compare the performance, efficiency, accuracy, scalability, and other aspects of the methods proposed in this research paper. Evaluate the advantages and disadvantages of this method in practical applications. Comparisons can be made through experimental results, case analysis, or theoretical derivations.
4. Experiments and Evaluations
    - 4.1 Introduction to Datasets
Introduce the commonly used experimental datasets in the research of \"the research status of a specific research direction\". Include the source, scale, characteristics, and applicable scope of the datasets. Explain why these datasets are selected for experimental research.
    - 4.2 Evaluation Metrics
List the main evaluation metrics used to evaluate the research results of \"the research status of a specific research direction\". Explain the meaning of each metric and its importance in the evaluation.
    - 4.3 Analysis of Experimental Results
Analyze in - depth the experimental results given in this research paper. Explain the reasons behind the results. Discuss the impact of the methods in this research paper on the experimental results.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure. """
        }},
    {"Chinese": {
        "非核心内容": """你的任务是从一篇以\"方法对比分析\"为主题的论文文献中提取关于介绍\"引言\"和\"任务介绍\"部分的关键信息，并结构化地呈现如下内容：
        **结构**
        1. 引言
        重要性阐述：请找出文献中关于\"方法对比分析\"在计算机领域重要性的描述。
        研究现状分析：提取当前\"方法对比分析\"的研究现状，包括已有的研究成果和存在的不足。
        实际应用需求：找出文献中提到的\"方法对比分析\"在实际应用中的具体需求。
        研究推动作用：强调\"方法对比分析\"对推动计算机领域发展的具体作用。
        2. 任务介绍
        2.1 基本概念与定义
        关键概念定义：请找出文献中对\"方法对比分析\"涉及的关键概念和术语的准确定义。
        统一理解：提取确保读者对\"方法对比分析\"有统一理解的描述，避免歧义。
        2.2 相关理论基础
        理论知识介绍：提取与\"方法对比分析\"及相关方法紧密相关的理论知识。
        数学模型：找出文献中提到的相关数学模型。
        算法原理：提取与\"方法对比分析\"相关的算法原理。
        理论支持：说明这些理论基础如何为后续方法的理解提供支持。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。""",
        "方法介绍": """你的任务是从一篇以\"方法对比分析\"为主题的论文文献中提取该论文中关于\"方法介绍\"和\"实验与评估\"部分的关键信息，并结构化地呈现如下内容：
        **结构**
        3. 方法介绍
        3.1 方法概述
        方法名称：请明确指出文献中提出的方法的名称。
        核心思想：简要描述该方法的核心思想和基本原理。
        3.2 方法分类
        方法类别：根据该方法的特点，将其归类到某一具体类别（例如监督学习、无监督学习、半监督学习等，也可以按其他的分类方式分类）。
        类别特征：描述该方法所属类别的典型特征。
        3.3 方法细节
        主要步骤：详细列出该方法的主要步骤和操作流程。
        关键技术：提取该方法中使用的关键技术和算法。
        实现方式：简要说明该方法的实现方式和工具。
        3.4 方法特点
        优势：总结该方法的主要优势和特点。
        局限性：指出该方法的潜在局限性和不足。
        3.5 对比要素
        方法架构：提取该方法的基本架构和组成模块。
        数据处理：描述该方法在数据处理方面的特点（如数据预处理、特征提取等）。
        方法性能：介绍该方法解决\"方法对比分析\"的效果
        4. 实验与评估

        4.1 数据集介绍

        介绍在\"方法对比分析\"问题中常用的实验数据集。
        包括数据集的来源、规模、特点以及适用范围。
        解释为何选择这些数据集进行实验研究。
        4.2 评估指标

        列举用于评估\"方法对比分析\"研究成果的主要评估指标。
        说明每个指标的含义以及在评估中的重要性。
        4.3 实验结果分析

        分析本篇论文给出的实验结果。
        对比其他论文的方法，探讨本篇论文方法对实验结果的影响。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。"""
    },
        "English": {
            "non-core content": """Your task is to extract the key information from the \"Introduction\" and \"Task Introduction\" sections of a research paper themed on \"a comparative analysis and summary of multiple methods\", and present it in a structured manner as follows:

**Structure**
1. Introduction
    - Significance Elaboration: Identify the descriptions in the literature regarding the significance of \"a comparative analysis and summary of multiple methods\" in the field of computer science.
    - Research Status Analysis: Extract the current research status of \"a comparative analysis and summary of multiple methods\", including existing research achievements and shortcomings.
    - Practical Application Requirements: Find out the specific requirements of \"a comparative analysis and summary of multiple methods\" in practical applications mentioned in the literature.
    - Research - Promoting Role: Highlight the specific role of \"a comparative analysis and summary of multiple methods\" in promoting the development of the field of computer science.
2. Task Introduction
    - 2.1 Basic Concepts and Definitions
        - Key Concept Definitions: Identify the accurate definitions of key concepts and terms related to \"a comparative analysis and summary of multiple methods\" in the literature.
        - Unified Understanding: Extract the descriptions that ensure readers have a unified understanding of \"a comparative analysis and summary of multiple methods\" to avoid ambiguity.
    - 2.2 Related Theoretical Foundations
        - Theoretical Knowledge Introduction: Extract the theoretical knowledge closely related to \"a comparative analysis and summary of multiple methods\" and relevant methods.
        - Mathematical Models: Identify the relevant mathematical models mentioned in the literature.
        - Algorithm Principles: Extract the algorithm principles related to \"a comparative analysis and summary of multiple methods\".
        - Theoretical Support: Explain how these theoretical foundations provide support for the understanding of subsequent methods.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure.""",
            "method introduction": """Your task is to extract the key information from the \"Method Introduction\" and \"Experiment and Evaluation\" sections of a research paper themed on \"a comparative analysis and summary of multiple methods\", and present it in a structured manner as follows:

**Structure**
3. Method Introduction
    - 3.1 Method Overview
        - Method Name: Clearly indicate the name of the method proposed in the literature.
        - Core Idea: Briefly describe the core idea and basic principles of the method.
    - 3.2 Method Classification
        - Method Category: According to the characteristics of the method, classify it into a specific category (such as supervised learning, unsupervised learning, semi - supervised learning, etc., or other classification methods can also be used).
        - Category Characteristics: Describe the typical characteristics of the category to which the method belongs.
    - 3.3 Method Details
        - Main Steps: List in detail the main steps and operational procedures of the method.
        - Key Technologies: Extract the key technologies and algorithms used in the method.
        - Implementation Approach: Briefly explain the implementation approach and tools of the method.
    - 3.4 Method Characteristics
        - Advantages: Summarize the main advantages and characteristics of the method.
        - Limitations: Point out the potential limitations and shortcomings of the method.
    - 3.5 Comparative Elements
        - Method Architecture: Extract the basic architecture and component modules of the method.
        - Data Processing: Describe the characteristics of the method in data processing (such as data pre - processing, feature extraction, etc.).
        - Method Performance: Introduce the effectiveness of the method in solving the \"a comparative analysis and summary of multiple methods\" problem.
4. Experiment and Evaluation
    - 4.1 Dataset Introduction
Introduce the commonly used experimental datasets in the \"a comparative analysis and summary of multiple methods\" problem. Include the source, scale, characteristics, and application scope of the datasets. Explain why these datasets are selected for experimental research.
    - 4.2 Evaluation Metrics
List the main evaluation metrics used to evaluate the research results of \"a comparative analysis and summary of multiple methods\". Explain the meaning of each metric and its importance in the evaluation.
    - 4.3 Experimental Result Analysis
Analyze the experimental results presented in this paper. Compare with the methods of other papers and explore the impact of the method in this paper on the experimental results.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure."""
        }},
    {"Chinese": {
        "引言": """你的任务是从一篇以\"技术方法发展历程\"为主题的论文文献中提取该论文中\"引言\"和\"关键技术与概念\"部分，\"技术方法发展历程\"产生的\"应用影响\"以及其\"挑战与局限性\"的关键信息，并结构化地呈现如下内容：
        **结构**
        1 引言

        1.1 研究背景(Research Background)

        阐述\"技术方法发展历程\"出现的技术背景。
        如相关技术的发展状况、行业需求等。
        解释为什么\"技术方法发展历程\"在当前背景下受到关注，其研究的必要性。
        1.2 研究目标(Research Objectives)

        明确阐述研究要达成的目标。
        如系统梳理技术方法的发展历程、分析各阶段特点及影响因素。
        1.3 研究意义(Significance of Research)

        强调\"技术方法发展历程\"对技术领域的学术价值。
        如丰富理论体系、为后续研究提供参考。
        说明\"技术方法发展历程\"对实际应用的指导意义。
        如优化现有技术、推动行业创新。
        2 关键概念和技术 KEY CONCEPTS AND TECHNIQUES

        2.1 核心概念 Core Concepts

        深入解释\"技术方法发展历程\"涉及的核心概念。
        确保读者对基本原理有清晰理解。
        通过实例或类比帮助读者更好地掌握关键概念。
        2.2 基本技术 Fundamental Techniques

        详细介绍构成\"技术方法发展历程\"的基本技术组件或算法。
        分析其功能和工作机制。
        对比不同技术组件的优缺点，以及它们在整体技术方法中的协同作用。
        2.3 关键技术的发展 Evolution of Key Techniques

        阐述本篇论文对\"技术方法发展历程\"的组件或算法进行的改进
        分析改进的原因和目标。
        跟踪\"技术方法发展历程\"的组件或算法随时间的演变过程。
        探讨技术演变对整个技术方法性能、适用范围等方面的影响。
        5 应用和影响

        5.1 应用领域

        根据本篇论文介绍\"技术方法发展历程\"在各个领域的应用情况。
        针对每个应用领域，详细阐述\"技术方法发展历程\"如何解决特定问题，带来的效益和价值提升。
        5.2 各领域影响

        根据本篇论文分析\"技术方法发展历程\"在各应用领域产生的广泛影响。
        包括对工作流程、效率、准确性等方面的改变。
        探讨技术方法应用引发的连锁反应，如推动相关技术发展、催生新的研究方向。
        5.3 跨领域影响

        根据本篇论文研究\"技术方法发展历程\"在不同应用领域之间的相互影响和迁移。
        如技术借鉴、融合创新。
        预测跨领域应用趋势对\"技术方法发展历程\"未来发展的影响，以及可能带来的新挑战和机遇。
        6 挑战与局限性(Challenges and Limitations)

        6.1 技术挑战(Technical Challenges)

        深入剖析\"技术方法发展历程\"在发展过程中面临的技术难题。
        如算法复杂度、计算资源需求等。
        分析这些技术挑战对技术进一步发展和推广应用的阻碍程度。
        6.2 实际限制

        探讨\"技术方法发展历程\"在实际应用中遇到的限制。
        如成本效益、可扩展性、可解释性等问题。
        研究实际局限性对技术方法在不同规模和场景下应用的影响。
        6.3 研究空白与未来发展方向

        明确当前研究在\"技术方法发展历程\"领域尚未解决的问题和存在的研究空白。
        基于挑战和局限分析，提出未来可能的研究方向和突破点，以推动技术方法的持续发展。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。""",
        "技术方法介绍": """你的任务是从一篇以\"技术方法发展历程\"为主题的论文文献中提取该论文中关于\"技术方法介绍\"及其\"历史视角\"部分的关键信息，并结构化地呈现如下内容：
        **结构**
        4. 方法介绍
        4.1 方法概述
        方法名称：请明确指出文献中提出的方法的名称。
        核心思想：简要描述该方法的核心思想和基本原理。
        4.2 方法分类
        方法类别：根据该方法的特点，将其归类到某一具体类别（例如监督学习、无监督学习、半监督学习等，也可以按其他的分类方式分类）。
        类别特征：描述该方法所属类别的典型特征。
        4.3 方法细节
        主要步骤：详细列出该方法的主要步骤和操作流程。
        关键技术：提取该方法中使用的关键技术和算法。
        实现方式：简要说明该方法的实现方式和工具。
        4.4 方法特点
        优势：总结该方法的主要优势和特点。
        局限性：指出该方法的潜在局限性和不足。
        4.5 对比要素
        方法架构：提取该方法的基本架构和组成模块。
        数据处理：描述该方法在数据处理方面的特点（如数据预处理、特征提取等）。
        方法性能：介绍该方法解决\"方法对比分析\"的效果
        5 历史视角(HISTORICAL PERSPECTIVE)
        5.1 技术起源(Origins of the Technology)
        依据本篇论文追溯\"技术方法发展历程\"的起源，介绍早期相关理论、思想或原型的形成。
        分析起源阶段的关键因素和推动力量，如学术研究成果、实际需求驱动等。
        5.2 早期发展阶段(Early Development Stages)
        详细描述\"技术方法发展历程\"在早期的发展状况，包括关键技术突破、标志性研究成果。
        探讨早期发展阶段\"技术方法发展历程\"的特点、应用场景及局限性。
        5.3 演化过程中的里程碑(Milestones in the Evolution)
        梳理\"技术方法发展历程\"发展过程中的重要里程碑事件，如算法改进、模型创新等。
        分析每个里程碑对技术发展方向的影响，以及在技术演进中的承上启下作用。

        以下是给定的论文文献
        **论文文献**
        \"\"\"
        %s
        \"\"\"
        请确保提取的信息准确、全面，并按照上述结构进行呈现。"""
    },
        "English": {
            "introduction": """Your task is to extract the key information from the \"Introduction\", \"Key Technologies and Concepts\" sections, as well as the \"Application Impact\" and \"Challenges and Limitations\" of the \"the development history of a certain technical method\", and present it in a structured manner as follows:

**Structure**
1. Introduction
    - 1.1 Research Background
Elaborate on the technical background of the emergence of the \"the development history of a certain technical method\", such as the development status of related technologies and industry demands. Explain why the \"the development history of a certain technical method\" has drawn attention in the current context and the necessity of its research.
    - 1.2 Research Objectives
Clearly state the goals that the research aims to achieve, such as systematically reviewing the development history of technical methods, analyzing the characteristics and influencing factors of each stage.
    - 1.3 Significance of Research
Highlight the academic value of the \"the development history of a certain technical method\" in the technical field, such as enriching the theoretical system and providing reference for subsequent research. Illustrate the guiding significance of the \"the development history of a certain technical method\" for practical applications, such as optimizing existing technologies and promoting industry innovation.
2. KEY CONCEPTS AND TECHNIQUES
    - 2.1 Core Concepts
Deeply explain the core concepts involved in the \"the development history of a certain technical method\". Ensure that readers have a clear understanding of the basic principles. Use examples or analogies to help readers better grasp the key concepts.
    - 2.2 Fundamental Techniques
Introduce in detail the basic technical components or algorithms that constitute the \"the development history of a certain technical method\". Analyze their functions and working mechanisms. Compare the advantages and disadvantages of different technical components and their synergistic effects in the overall technical method.
    - 2.3 Evolution of Key Techniques
Elaborate on the improvements made in this paper to the components or algorithms of the \"the development history of a certain technical method\". Analyze the reasons and goals of the improvements. Trace the evolutionary process of the components or algorithms of the \"Development History of Technical Methods\" over time. Explore the impact of technological evolution on aspects such as the performance and application scope of the entire technical method.
5. Application and Impact
    - 5.1 Application Areas
Based on this paper, introduce the application of the \"the development history of a certain technical method\" in various fields. For each application field, elaborate on how the \"the development history of a certain technical method\" solves specific problems and the resulting benefits and value enhancements.
    - 5.2 Influence in Each Field
Based on this paper, analyze the extensive influence of the \"the development history of a certain technical method\" in various application fields, including changes in work processes, efficiency, accuracy, etc. Explore the chain reactions triggered by the application of technical methods, such as promoting the development of related technologies and spawning new research directions.
    - 5.3 Cross - field Influence
Based on the research of this paper, study the mutual influence and transfer of the \"the development history of a certain technical method\" among different application fields, such as technical reference and integrative innovation. Predict the impact of cross - field application trends on the future development of the \"the development history of a certain technical method\", as well as the possible new challenges and opportunities.
6. Challenges and Limitations
    - 6.1 Technical Challenges
Thoroughly analyze the technical problems faced by the \"the development history of a certain technical method\" during its development, such as algorithm complexity and computational resource requirements. Analyze the degree of obstruction of these technical challenges to the further development and widespread application of the technology.
    - 6.2 Practical Limitations
Explore the limitations encountered by the \"the development history of a certain technical method\" in practical applications, such as cost - effectiveness, scalability, interpretability, etc. Study the impact of practical limitations on the application of technical methods in different scales and scenarios.
    - 6.3 Research Gaps and Future Development Directions
Identify the unsolved problems and existing research gaps in the current research of the \"the development history of a certain technical method\" field. Based on the analysis of challenges and limitations, propose possible future research directions and breakthrough points to promote the continuous development of technical methods.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure.""",
            "Introduction of Technical Methods": """Your task is to extract the key information from the \"Technical Method Introduction\" and its \"Historical Perspective\" sections of a research paper themed on \"the development history of a certain technical method\", and present it in a structured manner as follows:

**Structure**
4. Method Introduction
    - 4.1 Method Overview
        - Method Name: Clearly indicate the name of the method proposed in the literature.
        - Core Idea: Briefly describe the core idea and basic principles of the method.
    - 4.2 Method Classification
        - Method Category: According to the characteristics of the method, classify it into a specific category (such as supervised learning, unsupervised learning, semi - supervised learning, etc., or other classification methods can also be used).
        - Category Characteristics: Describe the typical characteristics of the category to which the method belongs.
    - 4.3 Method Details
        - Main Steps: List in detail the main steps and operational procedures of the method.
        - Key Technologies: Extract the key technologies and algorithms used in the method.
        - Implementation Approach: Briefly explain the implementation approach and tools of the method.
    - 4.4 Method Characteristics
        - Advantages: Summarize the main advantages and characteristics of the method.
        - Limitations: Point out the potential limitations and shortcomings of the method.
    - 4.5 Comparative Elements
        - Method Architecture: Extract the basic architecture and component modules of the method.
        - Data Processing: Describe the characteristics of the method in data processing (such as data pre - processing, feature extraction, etc.).
        - Method Performance: Introduce the effectiveness of the method in solving \"method comparative analysis\".

5. HISTORICAL PERSPECTIVE
    - 5.1 Origins of the Technology
Based on this paper, trace back to the origin of \"the development history of a certain technical method\", and introduce the formation of early related theories, ideas, or prototypes. Analyze the key factors and driving forces in the origin stage, such as academic research achievements, practical demand drivers, etc.
    - 5.2 Early Development Stages
Describe in detail the development of \"the development history of a certain technical method\" in the early stage, including key technological breakthroughs and landmark research achievements. Explore the characteristics, application scenarios, and limitations of \"the development history of a certain technical method\" in the early development stage.
    - 5.3 Milestones in the Evolution
Sort out the important milestone events in the development of \"the development history of a certain technical method\", such as algorithm improvements, model innovations, etc. Analyze the impact of each milestone on the direction of technological development, as well as its connecting role in the technological evolution.

The following is the given research paper:

**Research Paper**
\"\"\"
%s
\"\"\"
Please ensure that the extracted information is accurate and comprehensive, and presented in accordance with the above structure."""
        }}
]
extract_key_entity_prompts = [
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **核心概念变体**：识别并提取与\"{综述主题}\"相关的核心概念的变体或衍生概念。
2. **相关技术概念**：识别并提取与\"{综述主题}\"相关的技术概念或方法。
3. **应用任务领域**：识别并提取\"{综述主题}\"所应用的具体任务或领域。
4. **改进方法类别**：识别并提取针对\"{综述主题}\"的改进方法或技术。
5. **理论支撑领域**：识别并提取支撑\"{综述主题}\"的理论基础或相关学科。
6. **评估指标类型**：识别并提取用于评估\"{综述主题}\"性能的指标或标准名称。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Variants of Core Concepts**: Identify and extract the variants or derivative concepts of the core concepts related to the "{综述主题}".
2. **Related Technical Concepts**: Identify and extract the technical concepts or methods related to the "{综述主题}".
3. **Application Task Domains**: Identify and extract the specific tasks or domains to which the "{综述主题}" is applied.
4. **Categories of Improvement Methods**: Identify and extract the improvement methods or technologies for the "{综述主题}".
5. **Theoretical Support Domains**: Identify and extract the theoretical basis or related disciplines that support the "{综述主题}".
6. **Types of Evaluation Indicators**: Identify and extract the names of the indicators or criteria used to evaluate the performance of the "{综述主题}".

**Paper Excerpt**:

"\"\"%s\"\""

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **相关研究领域**：识别并提取与\"{综述主题}\"密切相关的其他研究领域或子领域。
2. **方法类别名称**：识别并提取在\"{综述主题}\"中使用的各类方法或算法的名称。
3. **关键技术**：识别并提取支撑\"{综述主题}\"发展的关键技术或核心组件。
4. **评估基准集**：识别并提取用于评估\"{综述主题}\"性能的常用数据集或基准测试。
5. **挑战类别**：识别并提取\"{综述主题}\"当前面临的主要挑战或问题名称。
6. **典型应用场景**：识别并提取\"{综述主题}\"在实际中应用的典型场景或案例。
7. **研究热点**：识别并提取当前\"{综述主题}\"领域内的研究热点或前沿方向。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Related Research Fields**: Identify and extract other research fields or subfields that are closely related to the "{综述主题}".
2. **Names of Method Categories**: Identify and extract the names of various methods or algorithms used in the "{综述主题}".
3. **Key Technologies**: Identify and extract the key technologies or core components that support the development of the "{综述主题}".
4. **Evaluation Benchmark Sets**: Identify and extract the commonly used datasets or benchmark tests for evaluating the performance of the "{综述主题}".
5. **Categories of Challenges**: Identify and extract the names of the main challenges or problems currently faced by the "{综述主题}".
6. **Typical Application Scenarios**: Identify and extract the typical scenarios or cases where the "{综述主题}" is applied in practice.
7. **Research Hotspots**: Identify and extract the current research hotspots or cutting-edge directions within the field of the "{综述主题}".

**Paper Excerpt**:

\"\"\"%s\"\"\"

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **方法类别**：识别并提取在\"{综述主题}\"中使用的方法或算法的类别。
2. **对比维度**：识别并提取在\"{综述主题}\"中用于比较不同方法的维度或标准，例如：性能表现的多个方面、运行速度、内存占用等。
3. **应用场景**：识别并提取\"{综述主题}\"所适用的具体应用场景或案例。
4. **评估任务**：识别并提取在\"{综述主题}\"中用于评估不同方法性能的具体任务或测试。
5. **相关技术**：识别并提取与\"{综述主题}\"中相关的关键技术或支持技术。
6. **局限类型**：识别并提取不同方法在\"{综述主题}\"应用中存在的局限性或不足。
7. **方法改进方向**：识别并提取在\"{综述主题}\"上的方法改进方向。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Method Categories**: Identify and extract the categories of methods or algorithms used in the "{综述主题}".
2. **Comparison Dimensions**: Identify and extract the dimensions or criteria used for comparing different methods in the "{综述主题}", such as various aspects of performance, running speed, memory usage, etc.
3. **Application Scenarios**: Identify and extract the specific application scenarios or cases to which the "{综述主题}" is applicable.
4. **Evaluation Tasks**: Identify and extract the specific tasks or tests used to evaluate the performance of different methods in the "{综述主题}".
5. **Related Technologies**: Identify and extract the key technologies or supporting technologies related to the "{综述主题}".
6. **Types of Limitations**: Identify and extract the limitations or deficiencies of different methods in the application of the "{综述主题}".
7. **Directions for Method Improvement**: Identify and extract the directions for improving the methods in the "{综述主题}".

**Paper Excerpt**:

"\"\"%s\"\""

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **技术路线**：识别并提取\"{综述主题}\"发展过程中形成的主要技术路线或演进路径。
2. **里程碑模型**：识别并提取在\"{综述主题}\"发展历程中具有标志性意义的模型或算法。
3. **关键技术**：识别并提取支撑\"{综述主题}\"发展的关键技术或核心组件。
4. **评估体系**：识别并提取用于评估\"{综述主题}\"性能的指标体系或评估方法。
5. **应用领域**：识别并提取\"{综述主题}\"在实际中应用的领域或行业。
6. **瓶颈类别**：识别并提取\"{综述主题}\"当前面临的主要瓶颈或限制因素。
7. **研究趋势**：识别并提取在\"{综述主题}\"上的研究趋势。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Technical Routes**: Identify and extract the main technical routes or evolutionary paths formed during the development process of the "{综述主题}".
2. **Milestone Models**: Identify and extract the models or algorithms that are of landmark significance in the development history of the "{综述主题}".
3. **Key Technologies**: Identify and extract the key technologies or core components that support the development of the "{综述主题}".
4. **Evaluation Systems**: Identify and extract the indicator systems or evaluation methods used to evaluate the performance of the "{综述主题}".
5. **Application Fields**: Identify and extract the fields or industries where the "{综述主题}" is actually applied.
6. **Categories of Bottlenecks**: Identify and extract the main bottlenecks or limiting factors currently faced by the "{综述主题}".
7. **Research Trends**: Identify and extract the research trends in the "{综述主题}".

**Paper Excerpt**:

"\"\"%s\"\""

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
]
extract_key_entity_prompts_v2 = [
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **核心概念变体**：识别并提取与\"{综述主题}\"相关的核心概念的变体或衍生概念。
2. **相关技术概念**：识别并提取与\"{综述主题}\"相关的技术概念或方法。
3. **应用任务领域**：识别并提取\"{综述主题}\"所应用的具体任务或领域。
4. **改进方法类别**：识别并提取针对\"{综述主题}\"的改进方法或技术。
5. **理论支撑领域**：识别并提取支撑\"{综述主题}\"的理论基础或相关学科。
6. **评估指标类型**：识别并提取用于评估\"{综述主题}\"性能的指标或标准名称。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Variants of Core Concepts**: Identify and extract the variants or derivative concepts of the core concepts related to the "{综述主题}".
2. **Related Technical Concepts**: Identify and extract the technical concepts or methods related to the "{综述主题}".
3. **Application Task Domains**: Identify and extract the specific tasks or domains to which the "{综述主题}" is applied.
4. **Categories of Improvement Methods**: Identify and extract the improvement methods or technologies for the "{综述主题}".
5. **Theoretical Support Domains**: Identify and extract the theoretical basis or related disciplines that support the "{综述主题}".
6. **Types of Evaluation Indicators**: Identify and extract the names of the indicators or criteria used to evaluate the performance of the "{综述主题}".

**Paper Excerpt**:

"\"\"%s\"\""

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"相关的关键学术实体名称及其与\"{综述主题}\"的关系简述。提取的信息应包括但不限于以下类别：

1. **相关研究领域**：识别并提取与\"{综述主题}\"密切相关的其他研究领域或子领域。
2. **方法类别名称**：识别并提取在\"{综述主题}\"中使用的各类方法或算法的名称。
3. **关键技术**：识别并提取支撑\"{综述主题}\"发展的关键技术或核心组件。
4. **评估基准集**：识别并提取用于评估\"{综述主题}\"性能的常用数据集或基准测试。
5. **挑战类别**：识别并提取\"{综述主题}\"当前面临的主要挑战或问题名称。
6. **典型应用场景**：识别并提取\"{综述主题}\"在实际中应用的典型场景或案例。
7. **研究热点**：识别并提取当前\"{综述主题}\"领域内的研究热点或前沿方向。
**论文片段：**

\"\"\"%s\"\"\"

**注意**：
1. 在进行信息提取时，请务必为每一项提取的内容明确标注其所属的学术实体类别，同时写出该学术实体的具体名称，并对其与 “{综述主题}” 之间的关联进行简要描述。
2. 若论文片段中不存在某类信息，则无需对其进行提取操作。
3. 在进行学术实体提取时，请确保所提取的内容能够为主题为“{综述主题}”的综述论文大纲制定提供有效助力，符合这一条件方可进行提取操作。
请直接输出提取结果。
""",
        "English": """Please read the following paper excerpt carefully and extract the names of key academic entities related to the "{综述主题}" and a brief description of their relationship with the "{综述主题}" from it. The extracted information should include but not be limited to the following categories:

1. **Related Research Fields**: Identify and extract other research fields or subfields that are closely related to the "{综述主题}".
2. **Names of Method Categories**: Identify and extract the names of various methods or algorithms used in the "{综述主题}".
3. **Key Technologies**: Identify and extract the key technologies or core components that support the development of the "{综述主题}".
4. **Evaluation Benchmark Sets**: Identify and extract the commonly used datasets or benchmark tests for evaluating the performance of the "{综述主题}".
5. **Categories of Challenges**: Identify and extract the names of the main challenges or problems currently faced by the "{综述主题}".
6. **Typical Application Scenarios**: Identify and extract the typical scenarios or cases where the "{综述主题}" is applied in practice.
7. **Research Hotspots**: Identify and extract the current research hotspots or cutting-edge directions within the field of the "{综述主题}".

**Paper Excerpt**:

\"\"\"%s\"\"\"

**Note**:
1. When extracting information, please clearly mark the academic entity category to which each extracted item belongs, write down the specific name of the academic entity, and briefly describe the connection between it and "{综述主题}".
2. If there is no such type of information in the paper snippet, there is no need to perform the extraction operation.
3. When extracting academic entities, please ensure that the extracted content can provide effective assistance for the outline of the review paper with the topic "{Review Topic}", and only perform the extraction operation if it meets this condition.
Please directly output the extraction results. """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且对对比分析综述大纲构建具有明确价值的学术实体。
        
具体提取类别及要求：
1. **方法实体**：仅提取具有明确技术特征的算法/模型名称（如ViT视觉Transformer、DALL-E多模态模型）
2. **对比指标**：限定为可量化的技术参数（如参数量、 FLOPS、zero-shot准确率）或标准化评估协议（如Few-shot Learning Benchmark）
3. **应用场景**：聚焦具有技术适配性挑战的垂直场景（如卫星遥感图像解译、蛋白质结构预测）
4. **评测基准**：标注领域公认的测试集（如SQuAD问答数据集、COCO目标检测基准）
5. **支撑技术**：提取与对比分析直接相关的技术模块（如MoE混合专家架构、Prompt Tuning技术）
6. **技术瓶颈**：明确方法本身的机制性缺陷（如Transformer的长序列计算瓶颈、GAN的模式崩溃问题）
7. **改进策略**：提出具有可验证性的技术改进方向（如动态网络剪枝、领域自适应预训练）

**论文片段：**

\"\"\"%s\"\"\"

**提取标准：**
1. 每个实体需满足：
   - 名称为领域内公认的技术术语
   - 在"{综述主题}"主题下存在直接的对比分析价值
   - 能支撑大纲中章节的结构设计
2. 采用三级过滤机制：
   - 首先筛选出与{综述主题}直接相关的实体
   - 其次排除通用化描述（如"深度学习方法"）
   - 最终保留对进行对比分析有明确作用的实体
3. 输出格式要求：
   - 提取{实体类别}::{实体名称}::{关联描述}
   - 关联描述需简要说明该实体对于"{综述主题}"的对比分析价值
   - 无有效信息时输出空字典"{}"即可

请严格按上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please carefully read the following excerpt from the paper and extract the academic entities that are directly related to the "{综述主题}" and have clear value for constructing the outline of the comparative analysis review. 
Specific extraction categories and requirements:
1. **Method Entity**: Only extract algorithm/model names with clear technical characteristics (e.g., ViT visual Transformer, DALL-E multimodal model)
2. **Comparison Indicators**: Limit to quantifiable technical parameters (such as parameter quantity, FLOPS, zero-shot accuracy rate) or standardized evaluation protocols (such as Few-shot Learning Benchmark)
3. **Application Scenarios**: Focus on vertical scenarios with technical adaptability challenges (such as satellite remote sensing image interpretation, protein structure prediction)
4. **Evaluation Benchmarks**: Label the recognized test sets in the field (such as SQuAD question answering dataset, COCO object detection benchmark)
5. **Supporting Technologies**: Extract technical modules directly related to the comparative analysis (such as MoE hybrid expert architecture, Prompt Tuning technology)
6. **Technical Bottlenecks**: Clearly identify the mechanismic defects of the method itself (such as the long sequence calculation bottleneck of Transformer, the mode collapse problem of GAN)
7. **Improvement Strategies**: Propose verifiable technical improvement directions (such as dynamic network pruning, domain self-adaptive pre-training) 

**Fragment of the Thesis:**

\"\"\"%s\"\"\"

**Extraction Criteria:**
1. Each entity must meet the following conditions:
- The name should be a recognized technical term within the domain.
- It should have direct comparative analysis value under the "{综述主题}" category.
- It should support the structural design of the chapters in the outline.
2. Three-level filtering mechanism is adopted:
- Firstly, filter out entities directly related to "{综述主题}".
- Secondly, exclude generalized descriptions (such as "Deep Learning Methods").
- Finally, retain entities that have a clear role in comparative analysis.
3. Output format requirements:
- Extract {entity_category}:{entity_name}:{descriptive_linkage}
- The associated description should briefly explain the comparative analysis value of the entity for "{综述主题}"
- If there is no valid information, output an empty dictionary "{}" instead. 

Please strictly follow the above standards to output the structured extraction results. Directly output the results:"""
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且具有明确学术价值的关键实体。提取的信息应严格满足以下标准：

1. **强关联性**：实体必须与综述主题存在直接且明确的学术关联
2. **专业规范性**：实体名称需为领域内公认的专业术语或标准命名
3. **学术贡献性**：实体需对主题领域的发展具有显著推动作用或标志性意义

具体提取类别及要求：
1. **技术范式**：识别具有代际演进特征的技术体系（如：Transformer架构演进）
2. **标杆模型**：标注具有里程碑意义的算法/模型（如：GPT-4、BERT）
3. **核心技术**：提取支撑领域发展的关键技术模块（如：注意力机制、对比学习）
4. **评测标准**：记录被广泛采用的评估体系（如：GLUE基准、BLEU评分）
5. **垂直应用**：列举具有代表性的行业解决方案（如：医疗影像AI诊断系统）
6. **技术瓶颈**：提炼制约领域发展的关键限制因素（如：小样本学习能力不足）
7. **前沿方向**：捕捉具有学术潜力的研究趋势（如：多模态大模型研究）

**论文片段：**

\"\"\"%s\"\"\"

**注意事项：**
1. 若某类别在论文中无明确证据支撑，应完全省略该类别
2. 实体名称必须使用学术共同体公认的专业术语，禁止使用泛化表述
3. 关联描述需简要说明该实体如何推动{综述主题}发展或构成研究挑战
4. 仅当提取内容能直接服务于综述大纲的逻辑框架构建时才进行输出

请严格按照上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please carefully read the following excerpt from the paper and extract the key entities that are directly related to the "{综述主题}" and have clear academic value. The extracted information should strictly meet the following standards: 

1. **Strong Relevance**: The entity must have a direct and explicit academic connection with the review topic.
2. **Professional Normativity**: The entity name should be a recognized professional term or standard naming within the field.
3. **Academic Contribution**: The entity must have a significant promoting effect or landmark significance on the development of the subject area. 

Specific extraction categories and requirements:
1. **Technical Paradigm**: Identify technological systems with generational evolution characteristics (e.g., the evolution of Transformer architecture)
2. **Benchmark Model**: Label algorithms/models with milestone significance (e.g., GPT-4, BERT)
3. **Core Technology**: Extract key technical modules supporting the development of the field (e.g., attention mechanism, contrastive learning)
4. **Evaluation Criteria**: Record widely adopted evaluation systems (e.g., GLUE benchmark, BLEU score)
5. **Vertical Application**: List representative industry solutions (e.g., AI diagnosis system for medical imaging)
6. **Technical Bottleneck**: Extract key limiting factors hindering the development of the field (e.g., insufficient small sample learning ability)
7. **Frontier Direction**: Capture research trends with academic potential (e.g., research on multimodal large models) 

**Fragment of the Thesis:**

\"\"\"%s\"\"\"


**Notes:**
1. If there is no clear evidence supporting a certain category in the paper, it should be completely omitted.
2. Entity names must use professional terms recognized by the academic community. Generalized expressions are prohibited.
3. The descriptive linkage should briefly explain how the entity promotes the development of the {综述主题} or constitutes a research challenge.
4. Only when the extracted content can directly serve the logical framework construction of the review outline should it be output. 

Please strictly follow the above standards to output the structured extraction results. Directly output the result: """
    },
]
extract_key_entity_prompts_v2_zch = [
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且对技术概念类综述大纲构建具有明确价值的学术实体：

1. **核心概念变体**：提取同一核心概念的不同变体或版本 (如BERT的变体如RoBERTa、ALBERT)
2. **相关技术概念**：提取与该技术相关联的技术或概念 (如扩散模型中的采样方法)
3. **应用任务领域**：提取该技术适用的具体任务或领域 (如文本摘要、图像分类)
4. **改进方法类别**：提取用于改进该技术的方法类型 (如网络剪枝、知识蒸馏)
5. **理论支撑领域**：提取支撑该技术的理论基础或相关学科 (如深度学习、强化学习)
6. **评估指标类型**：提取用于评估该技术性能的指标或标准名称 (如准确率、精确率、召回率)
**论文片段：**

\"\"\"%s\"\"\"

**提取标准：**
1. 每个实体需满足：
   - 名称为领域内公认的技术术语
   - 在"{综述主题}"技术演进中具有里程碑意义或关键影响
   - 能体现技术发展的内在逻辑脉络（如继承关系/范式转变）
   - 能支撑大纲中章节的结构设计
2. 采用三级过滤机制：
   - 第一级：筛选与{综述主题}具有直接技术关联的实体
   - 第二级：排除以下三类：
     - 通用技术框架（如"神经网络"）
     - 临时性工程技巧（如"梯度裁剪"）
   - 第三级：保留具有以下任一特征的实体：
     - 形成技术谱系的关键节点
     - 引发范式变革的核心突破
     - 产生跨领域影响的代表方法
3. 输出格式要求：
   - 提取{实体类别}::{实体名称}::{关联描述}
   - 关联描述需简要说明该实体对于"{综述主题}"的对比分析价值
   - 无有效信息时输出空字典"{}"即可

请严格按上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please read the following paper fragments carefully and extract from them academic entities that are directly related to \"{综述主题}\" and have clear value to the construction of the technical concept class overview outline:

1. **Variants of Core Concepts**: Extract different variants or versions of the same core concept (e.g. BERT variants such as RoBERTa, ALBERT)。
2. **Related Technical Concepts**: Extract the technology or concept associated with the technology (such as sampling methods in diffusion models).
3. **Application Task Domains**: Extract specific tasks or domains for which the technology is applicable (such as text summary, image classification).
4. **Categories of Improvement Methods**: Extract the type of method used to improve the technology (e.g., network pruning, knowledge distillation).
5. **Theoretical Support Domains**: Extract the theoretical basis or related disciplines supporting the technology (such as deep learning, reinforcement learning).
6. **Types of Evaluation Indicators**: Extract the name of the indicator or standard used to evaluate the performance of the technology (such as accuracy, accuracy, recall).

**Paper Excerpt**:

"\"\"%s\"\""

**Extraction Criteria:**:
1. Each entity must meet:
   - The name is a recognized technical term in the field
   - A milestone or key influence in the evolution of "{综述主题}" technology
   - Can reflect the internal logical context of technological development (e.g. inheritance relationship/paradigm shift)
   - Can support the structural design of the chapters in the outline
2. Adopt three-level filtering mechanism:
   - Level 1: Filter entities that have a direct technical connection to {综述主题}
   - Level 2: The following three categories are excluded:
     - General technical framework (e.g. "Neural network")
     - Temporary engineering techniques (e.g. "gradient clipping")
   - Level 3: Entities with any of the following characteristics are retained:
     - Form key nodes of the technical spectrum
     - The core breakthrough that triggers paradigm change
     - A representative approach to cross-cutting impact
3. Output format requirements:
   - Extract {entity_category}:{entity_name}:{descriptive_linkage}
   - The association description should briefly describe the comparative analysis value of the entity to "{review topic}"
   - If there is no valid information, output empty dictionary "{}"

Please output the structured extraction results strictly according to the above standards, and directly output the results:"""
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且对研究现状类综述大纲具有明确价值的学术实体：

1. **相关研究领域**：识别并提取与\"{综述主题}\"密切相关的其他研究领域或子领域。
2. **方法类别名称**：识别并提取在\"{综述主题}\"中使用的各类方法或算法的名称。
3. **关键技术**：识别并提取支撑\"{综述主题}\"发展的关键技术或核心组件。
4. **评估基准集**：识别并提取用于评估\"{综述主题}\"性能的常用数据集或基准测试。
5. **挑战类别**：识别并提取\"{综述主题}\"当前面临的主要挑战或问题名称。
6. **典型应用场景**：识别并提取\"{综述主题}\"在实际中应用的典型场景或案例。
7. **研究热点**：识别并提取当前\"{综述主题}\"领域内的研究热点或前沿方向。
**论文片段：**

\"\"\"%s\"\"\"

**提取标准：**
1. 每个实体需满足：
   - 能反映"{综述主题}"当前发展阶段的关键特征
   - 名称为领域内公认的技术术语
   - 能支撑大纲中章节的结构设计
2. 采用三级过滤机制：
   - 第一级：筛选与{综述主题}具有直接技术关联的实体
   - 第二级：排除三类非关键实体：
     - 通用技术组件（如残差连接）
     - 临时性工程技巧（如梯度裁剪）
     - 单一论文提出的未验证概念
   - 第三级：保留具有以下任一特征的实体：
     - 技术演进的关键节点（如Transformer架构的提出）
     - 当前研究热点的载体（如MoE架构）
     - 阻碍发展的典型瓶颈（如Scaling Law极限）
3. 输出格式要求：
   - 提取{实体类别}::{实体名称}::{关联描述}
   - 关联描述需简要说明该实体对于"{综述主题}"的对比分析价值
   - 无有效信息时输出空字典"{}"即可

请严格按上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please carefully read the following paper fragments and extract from them academic entities that are directly related to \"{综述主题}\" and have clear value to the status of research class overview outline:

1. **Related Research Fields**: Identify and extract other research fields or subfields that are closely related to the "{综述主题}".
2. **Names of Method Categories**: Identify and extract the names of various methods or algorithms used in the "{综述主题}".
3. **Key Technologies**: Identify and extract the key technologies or core components that support the development of the "{综述主题}".
4. **Evaluation Benchmark Sets**: Identify and extract the commonly used datasets or benchmark tests for evaluating the performance of the "{综述主题}".
5. **Categories of Challenges**: Identify and extract the names of the main challenges or problems currently faced by the "{综述主题}".
6. **Typical Application Scenarios**: Identify and extract the typical scenarios or cases where the "{综述主题}" is applied in practice.
7. **Research Hotspots**: Identify and extract the current research hotspots or cutting-edge directions within the field of the "{综述主题}".

**Paper Excerpt**:

\"\"\"%s\"\"\"

** Extraction standard: **
1. Each entity must meet:
   - Reflect the key features of the current development stage of {综述主题}
   - The name is a recognized technical term in the field
   - Can support the structural design of the chapters in the outline
2. Adopt three-level filtering mechanism:
   - Level 1: Filter entities that have a direct technical connection to {综述主题}
   - Level 2: Excludes three types of non-critical entities:
     - General technical components (e.g. residual connections)
     - Temporary engineering techniques (e.g. gradient clipping)
     - Unverified concepts presented in a single paper
   - Level 3: Entities with any of the following characteristics are retained:
     - Key nodes in technology evolution (such as Transformer architecture)
     - Current research focus vectors (such as MoE architecture)
     - Typical bottlenecks that hinder development (e.g. Scaling Law limits)
3. Output format requirements:
   - Extract {entity_category}:{entity_name}:{descriptive_linkage}
   - The association description should briefly describe the comparative analysis value of the entity to "{review topic}"
   - If there is no valid information, output empty dictionary "{}"

Please output the structured extraction results strictly according to the above standards, and directly output the results: """
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且对对比分析综述大纲构建具有明确价值的学术实体。

具体提取类别及要求：
1. **方法实体**：仅提取具有明确技术特征的算法/模型名称（如ViT视觉Transformer、DALL-E多模态模型）
2. **对比指标**：限定为可量化的技术参数（如参数量、 FLOPS、zero-shot准确率）或标准化评估协议（如Few-shot Learning Benchmark）
3. **应用场景**：聚焦具有技术适配性挑战的垂直场景（如卫星遥感图像解译、蛋白质结构预测）
4. **评测基准**：标注领域公认的测试集（如SQuAD问答数据集、COCO目标检测基准）
5. **支撑技术**：提取与对比分析直接相关的技术模块（如MoE混合专家架构、Prompt Tuning技术）
6. **技术瓶颈**：明确方法本身的机制性缺陷（如Transformer的长序列计算瓶颈、GAN的模式崩溃问题）
7. **改进策略**：提出具有可验证性的技术改进方向（如动态网络剪枝、领域自适应预训练）

**论文片段：**

\"\"\"%s\"\"\"

**提取标准：**
1. 每个实体需满足：
   - 名称为领域内公认的技术术语
   - 在"{综述主题}"主题下存在直接的对比分析价值
   - 能支撑大纲中章节的结构设计
2. 采用三级过滤机制：
   - 首先筛选出与{综述主题}直接相关的实体
   - 其次排除通用化描述（如"深度学习方法"）
   - 最终保留对进行对比分析有明确作用的实体
3. 输出格式要求：
   - 提取{实体类别}::{实体名称}::{关联描述}
   - 关联描述需简要说明该实体对于"{综述主题}"的对比分析价值
   - 无有效信息时输出空字典"{}"即可

请严格按上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please carefully read the following excerpt from the paper and extract the academic entities that are directly related to the "{综述主题}" and have clear value for constructing the outline of the comparative analysis review. 
Specific extraction categories and requirements:
1. **Method Entity**: Only extract algorithm/model names with clear technical characteristics (e.g., ViT visual Transformer, DALL-E multimodal model)
2. **Comparison Indicators**: Limit to quantifiable technical parameters (such as parameter quantity, FLOPS, zero-shot accuracy rate) or standardized evaluation protocols (such as Few-shot Learning Benchmark)
3. **Application Scenarios**: Focus on vertical scenarios with technical adaptability challenges (such as satellite remote sensing image interpretation, protein structure prediction)
4. **Evaluation Benchmarks**: Label the recognized test sets in the field (such as SQuAD question answering dataset, COCO object detection benchmark)
5. **Supporting Technologies**: Extract technical modules directly related to the comparative analysis (such as MoE hybrid expert architecture, Prompt Tuning technology)
6. **Technical Bottlenecks**: Clearly identify the mechanismic defects of the method itself (such as the long sequence calculation bottleneck of Transformer, the mode collapse problem of GAN)
7. **Improvement Strategies**: Propose verifiable technical improvement directions (such as dynamic network pruning, domain self-adaptive pre-training) 

**Fragment of the Thesis:**

\"\"\"%s\"\"\"

**Extraction Criteria:**
1. Each entity must meet the following conditions:
- The name should be a recognized technical term within the domain.
- It should have direct comparative analysis value under the "{综述主题}" category.
- It should support the structural design of the chapters in the outline.
2. Three-level filtering mechanism is adopted:
- Firstly, filter out entities directly related to "{综述主题}".
- Secondly, exclude generalized descriptions (such as "Deep Learning Methods").
- Finally, retain entities that have a clear role in comparative analysis.
3. Output format requirements:
- Extract {entity_category}:{entity_name}:{descriptive_linkage}
- The associated description should briefly explain the comparative analysis value of the entity for "{综述主题}"
- If there is no valid information, output an empty dictionary "{}" instead. 

Please strictly follow the above standards to output the structured extraction results. Directly output the results:"""
    },
    {
        "Chinese": """请仔细阅读以下论文片段，并从中提取与\"{综述主题}\"直接相关且对技术发展脉络类综述大纲具有明确学术价值的关键实体：

1. **强关联性**：实体必须与综述主题存在直接且明确的学术关联
2. **专业规范性**：实体名称需为领域内公认的专业术语或标准命名
3. **学术贡献性**：实体需对主题领域的发展具有显著推动作用或标志性意义

具体提取类别及要求：
1. **技术范式**：识别具有范式革新意义的架构/方法论（如：Transformer架构演进）
2. **标杆模型**：标注具有里程碑意义的算法/模型（如：GPT-4、BERT）
3. **核心技术**：提取支撑领域发展的关键技术模块（如：注意力机制、对比学习）
4. **评测标准**：记录被广泛采用的评估体系（如：GLUE基准、BLEU评分）
5. **垂直应用**：列举具有代表性的行业解决方案（如：医疗影像AI诊断系统）
6. **技术瓶颈**：提炼制约领域发展的关键限制因素（如：小样本学习能力不足）
7. **前沿方向**：捕捉具有学术潜力的研究趋势（如：多模态大模型研究）

**论文片段：**

\"\"\"%s\"\"\"

**提取标准：**
1. 每个实体需满足：
   - 前沿方向需在近2年内出现（引用增长率>50%%）
   - 明确的技术创新维度（架构/训练/推理）
   - 能支撑大纲中章节的结构设计
2. 采用三级过滤机制：
   - 第一级筛选：基于技术树的关键路径识别
     - 保留：与"{综述主题}"技术链条直接相关的实体
     - 过滤：孤立技术点（如单一优化技巧）
   - 第二级过滤：基于学术价值的严格筛选
     -排除：
       - 通用组件（残差连接）
       - 工程技巧（梯度裁剪）
       - 未经验证的概念（论文原型）
   - 第三级验证：基于发展脉络的结构适配性
     - 保留：
       - 技术拐点标记（如Transformer取代RNN）
       - 当前研究热点载体（如MoE架构）
       - 公认瓶颈问题（如Scaling Law收敛）
3. 输出格式要求：
   - 提取{实体类别}::{实体名称}::{关联描述}
   - 关联描述需简要说明该实体对于"{综述主题}"的对比分析价值
   - 无有效信息时输出空字典"{}"即可

请严格按上述标准输出结构化提取结果，直接输出结果：
""",
        "English": """Please read the following paper fragments carefully and extract from them key entities that are directly related to \"{综述主题}\" and have clear academic value to the technical development context class overview outline: 

1. **Strong Relevance**: The entity must have a direct and explicit academic connection with the review topic.
2. **Professional Normativity**: The entity name should be a recognized professional term or standard naming within the field.
3. **Academic Contribution**: The entity must have a significant promoting effect or landmark significance on the development of the subject area. 

Specific extraction categories and requirements:
1. **Technical Paradigm**: Identify technological systems with generational evolution characteristics (e.g., the evolution of Transformer architecture)
2. **Benchmark Model**: Label algorithms/models with milestone significance (e.g., GPT-4, BERT)
3. **Core Technology**: Extract key technical modules supporting the development of the field (e.g., attention mechanism, contrastive learning)
4. **Evaluation Criteria**: Record widely adopted evaluation systems (e.g., GLUE benchmark, BLEU score)
5. **Vertical Application**: List representative industry solutions (e.g., AI diagnosis system for medical imaging)
6. **Technical Bottleneck**: Extract key limiting factors hindering the development of the field (e.g., insufficient small sample learning ability)
7. **Frontier Direction**: Capture research trends with academic potential (e.g., research on multimodal large models) 

**Fragment of the Thesis:**

\"\"\"%s\"\"\"


** Extraction standard: **
1. Each entity must meet:
   - Frontier direction must appear within the last 2 years (cited growth rate >50%%)
   - Clear dimensions of technological innovation (architecture/training/reasoning)
   - Can support the structural design of the chapters in the outline
2. Adopt three-level filtering mechanism:
   - First level screening: critical path identification based on technology tree
     - Reserved: entities directly related to the "{review subject}" technical chain
     - Filtering: isolated technical points (such as a single optimization tip)
   - Second level filtering: strict screening based on academic merit
     - Exclude:
       - Universal components (residual connection)
       - Engineering Skills (Gradient clipping)
       - Unproven concept (paper prototype)
   - Third level validation: structural fit based on development context
     - Reserved:
       - Technical inflection point markers (e.g. Transformer instead of RNN)
       - Current research focus vector (such as MoE architecture)
       - Recognized bottlenecks (e.g. Scaling Law convergence)
3. Output format requirements:
   - Extract {entity_category}:{entity_name}:{descriptive_linkage}
   - The association description should briefly describe the comparative analysis value of the entity to "{review topic}"
   - If there is no valid information, output empty dictionary "{}"

Please output the structured extraction results strictly according to the above standards, and directly output the results: """
    },
]
extract_claims_prompts = {
    "Chinese": """请仔细阅读以下参考文献段落，并从中提取有很大概率在撰写综述论文时被引用的重要内容。提取时请注意以下几点：
**注意**
主题相关：仅抽取与"%s"主题紧密相关的内容。
识别重点：包括但不限于实验主要结论、重要定义、主要数据集、主要研究方法、主要技术优势等。
格式：以"- "开头，分条列出每一条可能被引用的内容。不要输出任何分析语句。
声明过滤：避免将普适性语句、行文逻辑描述、实验细节、模型方法细节、实验技巧等不重要的信息提取出来。
代词替换：将文段中的代词替换为具体的名词或者代号。

**输出形式示例**
- 图像恢复旨在从退化图像中生成高质量图像。
- 早期大多数深度网络是为特定的图像恢复任务设计的，例如使用通道注意力机制构建基于CNN的超分辨率网络、通过输入多个下采样子图像和噪声水平图构建去噪网络、利用残差连接构建去雨网络、使用CNN估计传输率和反转大气散射模型实现图像去雾、设计多尺度网络进行图像去模糊。
- 图像恢复的主流网络有 U 形编解码器、普通残差嵌套结构、多阶段渐进式结构三种架构类型，其示意图在补充材料中。

**参考文献段落**
\"\"\"
%s
\"\"\"
注意：过滤掉细节内容，挑选出重要主干内容，""",

    "English": """Please carefully read the following reference paragraph and extract the important content that is highly likely to be cited when writing a review paper. Please pay attention to the following points when extracting:
**notes**
- Topic relevance: Only extract content closely related to the '%s' topic.
- Key identification: This includes but is not limited to the main conclusions of experiments, important definitions, major datasets, main research methods, and key technological advantages.
- Format: Start each point with '- ' and list each extractable point. Do not output any analysis statements.
- Filtering declaration: Avoid extracting general statements, descriptions of writing logic, experimental details, model method details, experimental techniques, and other irrelevant information.
- Pronoun substitution: Replace pronouns in the text with specific nouns or code name.

**Output format example**
-Image restoration aims to generate high-quality images from degraded images.
-Early deep networks were mostly designed for specific image restoration tasks, such as constructing CNN-based super-resolution networks using channel attention mechanisms, building denoising networks by inputting multiple downsampled sub-images and noise level maps, creating rain removal networks using residual connections, estimating transmission rates and reversing atmospheric scattering models for image dehazing, and designing multi-scale networks for image deblurring.
-The mainstream networks for image restoration include three architecture types: U-shaped encoder-decoder, ordinary residual nested structures, and multi-stage progressive structures, with schematic diagrams in the supplementary materials.

**Reference paragraph**
\"\"\"
%s
\"\"\"
Notes: Filter out the detailed content and extract the important core content  
    """
}


translate_prompts = {
    "Chinese": """将以下综述主题翻译为中文。
**综述主题**
\"%s\"
直接输出翻译结果。""",
    "English": """Translate the following survey topic into English.
**survey topic**
\"%s\"
Directly output the translation result."""}
generate_expressions_prompt = """
Please extract the academic entity of the following topic, and perform reasonable abbreviations, synonym conversions, and full-form expansions of abbreviations. Ultimately, generate up to three diverse English expressions that encompass the core theme. Each expression should be concise and representative, reflecting the core concept of the topic from different perspectives.
**Topic:** \"%s\"
Output format example:
["Expression 1", "Expression 2", ...]
Ensure that the output is a list of English expressions, with each expression articulated in English and covering various aspects of the topic.
Directly output the results:
"""
generate_keywords_prompt = """
**Instruction**
Your task is to generate one synonyms and two sub-categories that are highly related to the provided academic entity, ensuring they hold significant relevance and academic importance. The sub-categories should be crafted with a focus on maximizing their retrieval value for academic literature searches. It is essential that each sub-category meets the following criteria:

- They are highly specific, steering clear of general or oversimplified vocabulary.
- They do not consist of overly common single-word terms that could lead to a deluge of non-relevant search results.
- They are precise and directly connected to the subject matter, representing phrases that frequently used in academic research.
The academic entity is as follows:
\"\"\"
%s
\"\"\"
In generating the only synonym and the two sub-categories, prioritize those with strong retrieval value—phrases that are likely to yield a focused set of relevant academic papers.

Please provide the only synonym and the two sub-categories for the given academic entity:"""
outline_type_instructions = [
    {"Chinese": "聚焦核心概念的定义、构成要素及典型应用场景，梳理概念内涵与外延，构建理论框架体系。",
     "English": "Focus on the definition, constituent elements and typical application scenarios of core concepts, sort out the connotation and extension of the concepts, and construct a theoretical framework system."},
    {"Chinese": "系统总结领域核心研究方向、关键技术进展及当前面临的技术瓶颈与挑战，突出研究空白与创新机遇。",
     "English": "Systematically summarize the core research directions, key technology progress and current technical bottlenecks and challenges in the field, highlighting the research gaps and innovation opportunities."},
    {"Chinese": "从技术原理、性能指标、应用场景等多维度系统对比不同方法的优缺点，构建分类评价体系并提供决策参考。",
     "English": "Systematically compare the advantages and disadvantages of different methods from multiple dimensions such as technical principles, performance indicators, and application scenarios, construct a classification evaluation system and provide decision-making references."},
    {"Chinese": "按时间线或技术演进路径划分阶段，分析各阶段技术突破及驱动因素，揭示技术发展规律与未来演进方向。",
     "English": "Divide stages according to the timeline or technical evolution path, analyze the technological breakthroughs and driving factors in each stage, and reveal the development laws and future evolution directions of the technology."}
]
generate_outline_prompts = {
    "Chinese": """请精心构思并生成一个覆盖度高、逻辑严密、内容详实的综述论文大纲。主题为\"%s\"，综述类型为\"%s\"。

# 样例输出（新增带编号示例）：
\"\"\"
%s
\"\"\"

# 带有编号的参考关键信息：
%s

## 任务要求：
1. 生成综述大纲时需在每个章节标题后用方括号标注该章节主要参考的关键信息编号（如章节涉及多个信息，编号用逗号分隔）
2. 基于主题、综述类型及关键信息构建大纲，需满足：
   - 覆盖度高：全面涵盖领域核心内容
   - 逻辑严密：按从基础到前沿的递进结构展开
   - 内容详实：对重要子领域/方法进行三级细分
3. 参考样例输出的格式规范，确保编号标注准确

## 注意事项：
- %s
- 禁止直接罗列参考文献条目
- 每个章节标题后必须包含对应编号
- 编号需与关键信息列表准确对应

请直接输出符合要求的综述大纲：
    """,
    "English": """Please carefully conceive and generate a comprehensive review paper outline with high coverage, logical rigor, and detailed content. The theme is "%s", and the review type is "%s".

# Sample Output (with numbered examples):
\"\"\"
%s
\"\"\"

# Numbered Reference Key Information:
%s

## Task Requirements:
1. When generating the review outline, annotate the main reference key information numbers in square brackets after each section title (use commas to separate multiple numbers if a section references multiple items).
2. Construct the outline based on the theme, review type, and key information, ensuring:
   - High coverage: Comprehensive inclusion of core domain content
   - Logical rigor: Progressive structure from foundational to cutting-edge topics
   - Detailed content: Subdivision of sub-domains/methods into three or more levels
3. Follow the formatting specifications of the sample output to ensure accurate numbering.

## Notes:
- %s
- Directly listing reference entries is prohibited
- Each section title must include corresponding numbering
- Numbers must precisely match the key information list

Please directly output the review outline that meets the requirements:
"""
}
generate_outline_prompts_v2 = {
    "Chinese": """请精心构思并生成一个覆盖度高、逻辑严密、内容详实的综述论文大纲。主题为\"%s\"，综述类型为\"%s\"。

# 样例输出：
%s


# 参考关键信息：
%s

## 任务要求：
基于主题、综述类型及关键信息构建大纲，需满足：
   - 覆盖度高：全面涵盖领域核心内容
   - 逻辑严密：按从基础到前沿的递进结构展开
   - 内容详实：对子领域/方法进行三级以上细分

## 注意事项：
- 禁止直接罗列参考文献条目
- %s

请直接输出符合要求的综述大纲：
    """,
    "English": """Please meticulously conceive and formulate a comprehensive, logically coherent, and detailed outline for a review paper. The topic is "%s" and the survey type is "%s". 

# Sample Output: 
\"\"\"
%s
\"\"\"


# Reference Key Information:
%s


## Task Requirements:
Construct an outline based on the topic, review type, and key information, ensuring:
- Comprehensive coverage: Fully encompass the core content of the field
- Logical coherence: Develop in a progressive structure from basic to cutting-edge
- Detailed content: Conduct three-level or higher subdivision of subfields/methods 

## Important Notes:
- It is strictly prohibited to list the reference entries directly. 
- %s


Please output the review outline that meets the requirements directly:
"""
}
merge_outlines_prompts = {
    "Chinese": """你是一位擅长整合和优化大纲的文献工作者。请根据以下要求将多个综述大纲整合成一个更清晰、全面且逻辑合理的大纲。 
# 输入大纲
%s

# 样例输出：
%s


## 任务要求：
    - **整合大纲**：将输入的多个大纲整合为一个统一的大纲，确保内容全面覆盖多个输入大纲的关键点。
    - **优化结构**：调整大纲的层次结构，使其逻辑更清晰、条理更分明。
    - **去重与补充**：去除重复内容，补充遗漏的关键点，确保大纲的完整性。
    - **语言简洁**：使用简洁明了的语言表达每个条目，避免冗余。
    - **最小修改原则**: 在满足上述要求的前提下，尽量保证最终生成的大纲所使用的章节标题与某一输入大纲中的章节标题相同。
    - 生成的大纲需要包含输入大纲的主要内容，另外，如果某个章节包含超过5个子章节，需要对该章节的内容进行合并、整合。

## 注意事项：
    - 禁止生成过长的大纲
    - 禁止某个章节拥有超过5个子章节
    - 禁止直接罗列参考文献条目
    - 禁止直接简单地将多个大纲的内容拼接或相加

请直接输出符合要求的综述大纲：
""",
    "English": """You are a literature worker skilled in integrating and optimizing outlines. Please integrate multiple review outlines into a clearer, more comprehensive, and logically sound outline according to the following requirements.

# Input Outlines
%s

# Sample Output:
%s

## Task Requirements:
    - **Integrate Outlines**: Combine multiple input outlines into a unified one, ensuring that the content comprehensively covers the key points of the multiple input outlines.
    - **Optimize Structure**: Adjust the hierarchical structure of the outline to make its logic clearer and more organized.
    - **Deduplicate and Supplement**: Remove duplicate content and supplement missing key points to ensure the integrity of the outline.
    - **Concise Language**: Use concise and clear language to express each item and avoid redundancy.
    - **Principle of Minimum Modification**: On the premise of meeting the above requirements, try to ensure that the chapter titles used in the final generated outline are the same as those in one of the input outlines.
    - The generated outline needs to contain the main content of the input outlines. In addition, if a chapter contains more than 5 sub - chapters, the content of that chapter needs to be merged and integrated.

## Precautions:
    - Prohibit the generation of overly long outlines.
    - Prohibit a chapter from having more than 5 sub - chapters.
    - Prohibit directly listing reference entries.
    - Prohibit simply splicing or adding the contents of multiple outlines directly.

Please directly output the review outline that meets the requirements:
"""
}
generate_description_prompts = {
    "Chinese": """请根据以下信息，为即将撰写的综述文献的特定章节生成一句核心主旨：
**拟定好的综述大纲**：
\"\"\"
%s
\"\"\"
**综述主题**：\"%s\"
**章节标题**：\"%s\"


## 要求：

- 核心主旨需简洁明了，概括该章节的核心内容和研究重点。
- 确保主旨与综述主题和章节标题紧密相关。
- 主旨应具备启发性和引导性，便于后续章节内容的展开。
- 请生成一句符合上述要求的核心主旨。

不要分析过程，请直接给出你生成的一句核心主旨：
""",
    "English": """Please generate a central theme sentence for a specific section of an upcoming survey literature based on the following information:

**Planned survey outline**:
\"\"\"
%s
\"\"\"
**Survey topic**: \"%s\"
**Section title**: \"%s\"

## Requirements:
- The central theme should be concise, clearly summarizing the core content and research focus of this section.
- Ensure that the theme is closely related to the review topic and the section title.
- The theme should be inspiring and guiding, facilitating the development of subsequent section content.
- Please generate a central theme sentence that meets the above requirements.

Do not analyze the process. Please directly provide a central theme sentence that you have generated. 
    """
}
fomatize_outline_prompt = {
    "Chinese": """## 任务描述：
请将以下论文提纲严格按照“- [章节编号] [章节标题]”的格式进行格式化，确保原提纲内容不变，仅调整格式。
尤其不要忘记在每个条目开头加上'-'符号。
## 格式示例：
- 3.2 长程依赖
- 6 多模态信息
- 5.1 迁移学习
- 4.1.1 预训练

## 待格式化论文大纲：
\"\"\"
%s
\"\"\"
直接输出格式化后的大纲结果：""",
    "English": """## Task Description:
Please format the following thesis outline strictly according to the format of "- [Chapter Number] [Chapter Title]", ensuring that the content of the original outline remains unchanged, and only the format is adjusted.
Especially, don't forget to add a '-' symbol at the beginning of each entry.
## Format Example:
- 3.2 Long-range Dependencies
- 6 Multimodal Information
- 5.1 Transfer Learning
- 4.1.1 Pre-training

## Thesis Outline to be Formatted:
\"\"\"
%s
\"\"\"
Directly output the formatted outline result:"""
}
generate_draft_prompts = {
    "Chinese": """
你是一位擅长撰写综述论文的文献工作者。请根据以下信息撰写综述论文中的一个章节。

## 综述论文主题： %s

## 综述大纲：
%s

## 所需撰写章节的标题：%s

## 所需撰写章节的核心主旨：%s

## 参考文献关键信息(参考文献编号以方括号标注)：
%s

## 任务要求：
- 撰写章节草案：根据提供的章节信息和整篇综述的大纲，撰写一个连贯、详实的章节草案。确保章节内容逻辑清晰，语言流畅，避免过度细分小节或出现章节编号混乱的问题。

- 引用参考文献：在章节草案中，如果某句话需要引用参考文献的关键内容作为支撑，请准确地将关键内容所属文本块的块编号标记出来，格式为：<sup>1</sup>，<sup>4</sup>等等。使用<sup>n</sup>格式在语句结尾处标注出准确的参考文献编号。

**具体步骤：**

1. 引入段落：根据大纲中对前一章节的描述，自然过渡到本章节的主题。

2. 主体内容：根据本章节的描述，详细展开论述，确保每个关键点都有充分的解释和支撑。在章节草案中，所有需要支撑材料才能得到的观点、事实、理论或研究结果的明确表述都务必使用上述格式引用参考文献。

3. 总结段落：总结本章节的主要观点，并为下一个章节做铺垫。

**示例形式：**
在本章节中，我们将深入探讨[章节主题]。如前所述，[简要回顾前一个章节的内容]... 根据文献显示，[引用内容]<sup>13</sup>。此外，[进一步论述]... 研究表明，[引用内容]<sup>7</sup><sup>13</sup>。综上所述，[总结本章节的主要观点]...

**注意事项：**
- 引用格式：确保所有引用参考文献的文本都使用<sup>n</sup>格式标注出准确的参考文献编号，如果同一段文本需要多个参考文献进行支撑，则以<sup>n_1</sup><sup>n_2</sup><sup>n_3</sup>格式进行引用。

- 章节结构：严禁在本章节中分出子章节。

- 引用完整性：在章节草案中，所有需要支撑材料才能得到的观点、事实、理论或研究结果的明确表述都务必引用参考文献，避免遗漏。

- 学术严谨性：保持学术写作的严谨性和规范性，避免过度概括或过分夸大参考文献中的内容。

请根据以上提示词，直接生成该章节的文本草案：""",
    "English": """You are a literature worker skilled in writing survey papers. Please write a section of a survey paper based on the following information.

## survey paper topic: %s

## survey outline:
%s

## Title of the section to be written: %s

## Central theme of the section to be written: %s

## Key information of references (reference numbers are marked in square brackets):
%s

## Task requirements:
- Write a section draft: Based on the provided section information and the outline of the entire survey, write a coherent and detailed section draft. Ensure that the content of the section is logically clear, the language is fluent, and avoid over - subdividing sub - sections or having problems with section numbering confusion.

- Cite references: In the section draft, if a sentence needs to cite key content from references as support, accurately mark the block number of the text block to which the key content belongs, in the format: <sup>1</sup>, <sup>4</sup>, etc. Use the <sup>n</sup> format to mark the accurate reference number at the end of the sentence.

**Specific steps**:
1. Introduction paragraph: Naturally transition to the theme of this section based on the description of the previous section in the outline.
2. Main content: Expand the discussion in detail according to the description of this section, ensuring that each key point is fully explained and supported. In the section draft, all explicit statements of viewpoints, facts, theories, or research results that require supporting materials must cite references using the above - mentioned format.
3. Summary paragraph: Summarize the main viewpoints of this section and pave the way for the next section.

**Example form**:
In this section, we will delve into [section topic]. As previously mentioned, [briefly survey the content of the previous section]... According to the literature, [cited content]<sup>13</sup>. In addition, [further discussion]... Research shows that [cited content]<sup>7</sup><sup>13</sup>. In conclusion, [summarize the main viewpoints of this section]...

**Precautions**:
- Citation format: Ensure that all texts citing references are marked with the accurate reference number in the <sup>n</sup> format. If the same text requires multiple references for support, use the <sup>n_1</sup><sup>n_2</sup><sup>n_3</sup> format for citation.
- Section structure: It is strictly prohibited to divide sub - sections within this section.
- Citation integrity: In the section draft, all explicit statements of viewpoints, facts, theories, or research results that require supporting materials must cite references to avoid omissions.
- Academic rigor: Maintain the rigor and standardization of academic writing, and avoid over - generalizing or exaggerating the content in the references.

Please directly generate the text draft of this section according to the above prompts:
    """
}
generate_2rd_draft_prompts = {
    "Chinese": """
请根据以下信息撰写一段综述性文本，用于连接章节 "%s" 与其子章节之间的内容。
### 子章节内容
\"\"\"
%s
\"\"\"

### 背景信息：

**综述论文主题：** %s

**综述大纲：** 
%s

### 参考文献关键信息： 
**文本块编号及内容：**
%s

- 章节标题：%s

### 任务要求：
明确主题：确定综述要阐述的主题 "%s" 及该章节的主题 "%s"。

介绍概念：解释与章节主题相关的基本概念。

描述现状（可选）：说明该领域当前的研究或应用现状，包括已有的模型、方法、技术等。

指出问题（可选）：分析当前存在的问题或挑战。

引出下文：为后续子章节的内容做铺垫，确保逻辑连贯。

引用参考文献：
在章节草案中，如果某句话需要引用参考文献的关键内容作为支撑，请准确地将关键内容所属文本块的块编号标记出来，格式为：<sup>1</sup>，<sup>4</sup>等等。使用<sup>n</sup>格式在语句结尾处标注出准确的参考文献编号。

### 示例形式
[主题名称] 是指 [主题的基本概念解释]。在 [主题] 领域，[主题相关的研究或应用情况，如在过去的时间里提出了哪些相关模型、方法等]。目前，[进一步阐述现状，如列举重要的模型、方法及其特点]。  
然而，当前 [主题] 仍面临一些问题，例如 [具体问题内容]<sup>2</sup><sup>13</sup>。这些问题对 [相关方面] 产生了 [影响内容]。

### 注意事项：
- 语言流畅性：确保章节内容逻辑清晰，语言流畅。
- 章节结构：严禁在本章节中分出子章节。
- 引用格式：确保所有引用参考文献的文本都使用<sup>n</sup>格式标注出准确的参考文献编号，如果同一段文本需要多个参考文献进行支撑，则以<sup>n_1</sup><sup>n_2</sup><sup>n_3</sup>格式进行引用。
- 引用完整性：在所有需要引用参考文献进行支撑的地方，务必引用参考文献，避免遗漏。
- 学术严谨性：保持学术写作的严谨性和规范性，避免过度概括或过分夸大参考文献中的内容。

请根据以上提示词，直接生成文本草案：
""",
    "English": """Please write a summary text based on the following information to connect the content between the chapter "%s" and its sub - chapters.
### Sub-chapter content
\"\"\"
%s
\"\"\"
### Background information:
**Topic of the survey paper:** %s
**survey outline:** 
%s
### Key information of references: 
**Text block number and content:**
%s
- Chapter title: %s
### Task requirements:
Clarify the topic: Determine the topic "%s" to be expounded in the survey and the topic of this chapter "%s".
Introduce concepts: Explain the basic concepts related to the chapter topic.
Describe the current situation (optional): Illustrate the current research or application status in this field, including existing models, methods, technologies, etc.
Point out problems (optional): Analyze the existing problems or challenges.
Lead to the following content: Pave the way for the content of the subsequent sub - chapters to ensure logical coherence.
Cite references:
In the chapter draft, if a sentence needs to cite the key content of a reference as support, accurately mark the block number of the text block to which the key content belongs, in the format: <sup>1</sup>, <sup>4</sup>, etc. Use the <sup>n</sup> format to mark the accurate reference number at the end of the statement.
### Example form
[Subject name] refers to [explanation of the basic concept of the subject]. In the field of [subject], [research or application situations related to the subject, such as what relevant models and methods have been proposed in the past]. Currently, [further elaborate on the current situation, such as listing important models, methods and their characteristics].
However, the current [subject] still faces some problems, such as [specific problem content]<sup>2</sup><sup>13</sup>. These problems have [impact content] on [related aspects].
### Precautions:
- Language fluency: Ensure that the chapter content is logically clear and the language is fluent.
- Chapter structure: It is strictly prohibited to divide sub - chapters in this chapter.
- Citation format: Ensure that all texts citing references are marked with the accurate reference number in the <sup>n</sup> format. If a paragraph of text requires multiple references for support, use the <sup>n_1</sup><sup>n_2</sup><sup>n_3</sup> format for citation.
- Citation integrity: Be sure to cite references wherever references are needed for support to avoid omissions.
- Academic rigor: Maintain the rigor and standardization of academic writing, and avoid over - generalization or over - exaggeration of the content in the references.

Please directly generate the text draft according to the above prompts:
    """
}
completion_draft_prompts = {
    "Chinese": """
请根据以下信息撰写一段综述性文本，用于连接章节 "%s" 和其子章节之间的内容。

## 子章节内容
%s
## 背景信息：
### 综述论文主题： %s

### 综述大纲：
%s

## 任务要求：
明确主题：确定要阐述的主题 "%s" 及该章节的主题 "%s"，确保主题清晰且与综述论文主题一致。

介绍概念：简要解释与该章节主题相关的基本概念，为读者提供必要的背景知识。

描述现状（可选）：概述该领域当前的研究或应用现状，包括已有的模型、方法、技术等，突出重要进展和成果。

指出问题（可选）：分析当前研究中存在的问题或挑战，明确领域内的局限性或未解决的难题。

引出下文：自然过渡到子章节的内容，为后续讨论做铺垫，确保逻辑连贯。

## 示例形式：
[主题名称] 是指 [主题的基本概念解释]。在 [主题] 领域，[主题相关的研究或应用情况，如在过去的时间里提出了哪些相关模型、方法等]。目前，[进一步阐述现状，如列举重要的模型、方法及其特点]。  
[若存在问题则描述问题：然而，当前 [主题] 仍面临一些问题，例如 [具体问题内容]。这些问题对 [相关方面] 产生了 [影响内容]。]  
[引出下文：在接下来的子章节中，我们将深入探讨 [子章节内容概述]，以期为 [主题] 的研究和应用提供新的思路和解决方案。]  
## 注意事项：
- 逻辑清晰：确保文本结构合理，内容层次分明，逐步引导读者理解主题。
- 章节结构：严禁在本章节中分出子章节。
- 语言流畅：使用简洁、准确的学术语言，避免冗长或模糊的表达。
- 主题一致性：确保文本内容与综述论文主题及章节主题紧密相关，避免偏离主题。
- 过渡自然：在描述现状、指出问题后，自然引出子章节内容，确保上下文衔接流畅。

请根据以上提示词，直接生成文本草案：""",
    "English": """Please write a summary text based on the following information to connect the content between the chapter "%s" and its sub - chapter.

### Sub-chapter content
%s

### Background information:
#### Topic of the survey paper: %s

#### survey outline:
%s

### Task requirements:
**Clarify the theme**: Determine the theme "%s" to be elaborated and the theme of this chapter "%s", ensuring that the theme is clear and consistent with the theme of the survey paper.

**Introduce concepts**: Briefly explain the basic concepts related to the theme of this chapter to provide readers with the necessary background knowledge.

**Describe the current situation (optional)**: Outline the current research or application status in this field, including existing models, methods, technologies, etc., highlighting important progress and achievements.

**Point out problems (optional)**: Analyze the existing problems or challenges in current research, and clarify the limitations or unsolved problems in the field.

**Lead to the following content**: Make a natural transition to the content of the sub - chapter, pave the way for subsequent discussions, and ensure logical coherence.

### Example form:
[Theme name] refers to [basic concept explanation of the theme]. In the field of [theme], [research or application situations related to the theme, such as what relevant models and methods have been proposed in the past]. Currently, [further elaborate on the current situation, such as listing important models, methods and their characteristics].
[If there are problems, describe the problems: However, the current [theme] still faces some problems. For example, [specific problem content]. These problems have had [impact content] on [related aspects].]
[Lead to the following content: In the following sub - chapter, we will deeply explore [sub - chapter content overview] in the hope of providing new ideas and solutions for the research and application of [theme].]

### Precautions:
- **Logical clarity**: Ensure a reasonable text structure, distinct content levels, and gradually guide readers to understand the theme.
- **Chapter structure**: Sub - chapters are strictly prohibited in this chapter.
- **Fluent language**: Use concise and accurate academic language, and avoid long - winded or ambiguous expressions.
- **Theme consistency**: Ensure that the text content is closely related to the theme of the survey paper and the chapter theme, and avoid deviating from the theme.
- **Natural transition**: After describing the current situation and pointing out problems, naturally lead to the content of the sub - chapter to ensure a smooth connection between the context.

Please directly generate a text draft according to the above prompt words:
    """
}
generate_draft_abstract = {
    "Chinese":
        """
### 背景信息
**综述论文主题：** %s
    
**综述大纲：** 
    %s
    
**章节次序及标题和文本：**
    %s
    
### 任务要求
-1.核心观点提炼： 从章节草案中提炼出本章节的核心观点和主要论点。
-2.关键信息概括： 概括章节中的关键信息，包括重要发现、理论支持、研究结论等。
-3.逻辑连贯性： 确保摘要内容逻辑清晰，语言简洁，能够独立成文。
-4.字数限制在350-400字之间

###示例形式
本章节综述了[章节主题]，回顾了该领域的最新进展，并分析了不同研究方法的优势与不足。首先，[核心观点1]被广泛讨论，研究表明[关键发现]，这一发现为[相关领域]的理论框架或实践应用提供了新的视角。接着，[核心观点2]深入探讨了[研究结论]，并与已有文献进行对比，进一步丰富了该领域的学术讨论。通过对比不同的研究方法和视角，本章节总结了[关键信息]，并指出了当前研究中的空白以及未来的研究机会。此外，文章还讨论了[理论支持]，为后续研究提供了理论依据和实践指导。
通过对现有文献的综合分析，本文总结了[章节核心总结]，为[后续章节或未来研究方向]提供了宝贵的见解与支持。

### 注意事项
-1.简洁明了： 摘要应简洁明了，避免冗长和重复，严禁分段。
-2.准确无误： 确保摘要内容准确无误，忠实于章节草案的原意。
-3.学术规范： 保持学术写作的严谨性和规范性，避免过度概括或夸大。

直接输出摘要生成结果。
        """,
    "English":
        """
### Background Information
**Survey Paper Topic：** %s
    
**Survey Outline：** 
    %s
    
**Chapter Order, Title, and Text：**
    %s
    
### Task Requirements
-1.Core Idea Extraction：Extract the core ideas and main arguments of the chapter from the draft.
-2.Key Information Summary： Summarize the key information from the chapter, including important findings, theoretical support, research conclusions, etc.
-3.Logical Coherence： Ensure the summary is logically clear, with concise language, and can stand alone.
-4.Keep the summary between 150-200 words.

###Example Format
This chapter surveys [chapter topic], highlights the latest advancements in the field, and analyzes the strengths and weaknesses of various research methods. First, [core idea 1] is widely discussed, with studies showing [key finding], which provides new perspectives for the theoretical framework or practical applications of [related field]. Next, [core idea 2] explores [research conclusion] in depth and compares it with existing literature, further enriching the academic discussion in the field. By comparing different research methods and perspectives, this chapter summarizes [key information] and identifies gaps in current research, as well as future research opportunities. In addition, the article discusses [theoretical support], providing a theoretical basis and practical guidance for future research.
Through a comprehensive analysis of the existing literature, this paper concludes [core summary of the chapter], offering valuable insights and support for [subsequent chapters or future research directions].

### notes
-1.Conciseness： The summary should be concise, avoiding length and repetition. Subsections are strictly prohibited.
-2.Accuracy： Ensure the summary is accurate and faithful to the original meaning of the chapter draft.
-3.Academic Standards： Maintain academic rigor and norms, avoiding overgeneralization or exaggeration.

Output the abstract generation result directly.
        """
}
generate_draft_conclusion = {
    "Chinese":
        """
### 背景信息：
**综述论文主题：** %s
    
**综述大纲：** 
    %s
    
**章节次序及标题和文本：**
    %s
    
### 任务要求
-1.强调研究的贡献与创新性：指出本综述文章在相关领域的贡献，包括理论上的创新、研究方法的进步，或者对实践应用的启示。突出本文对现有文献的补充或扩展。
-2.指出研究的局限性和挑战：分析当前研究的不足或局限性，识别存在的挑战，例如方法、数据、应用场景等方面的限制。这一部分有助于读者了解当前研究的边界与发展潜力。
-3.展望未来的研究方向：提出未来研究可能的发展方向或新兴领域，阐述如何克服当前局限性以推动该领域的进一步发展。可以讨论新的技术、理论框架或实践应用对研究的推动作用。
-4.语言简洁明了：总结段的语言应简洁、清晰，避免冗长和重复。内容要逻辑严谨，能够独立成文，并能准确传达综述文章的核心思想。
-5.字数在300-400之间

###注意事项
-1.简洁明了：确保结尾段简洁明了，避免重复前述内容。
-2.准确无误：结尾段内容应准确无误，忠实于文章的核心论点。
-3.学术规范：保持学术写作的严谨性和规范性，避免过度总结或夸大。
-4.未来展望：应具备前瞻性，并给读者提供新的思考或研究视角。

直接输出总结生成结果。
        """,
    "English":
        """
### Background Information：
**Survey Paper Topic：** %s
    
**Survey Outline：** 
    %s
    
**Chapter Order, Titles, and Text：**
    %s
    
### Task Requirements
-1.Emphasize the Contribution and Innovation of the Research：Highlight the contributions of this survey paper to the relevant field, including theoretical innovations, advancements in research methods, or insights into practical applications. Emphasize how this paper supplements or extends the existing literature.
-2.Identify Research Limitations and Challenges：Analyze the shortcomings or limitations of current research, identifying challenges such as constraints in methodology, data, or application scenarios. This section helps readers understand the boundaries and potential for future developments in the field.
-3.Outlook on Future Research Directions：Propose potential future research directions or emerging areas, explaining how overcoming current limitations can advance the field. Discuss how new technologies, theoretical frameworks, or practical applications could contribute to further research progress.
-4.Concise and Clear Language：The concluding section should be concise and clear, avoiding redundancy and unnecessary repetition. The content should be logically structured, capable of standing alone, and accurately convey the core ideas of the survey paper.
-5.Word Limit: 200-300 words.

###notes
-1.Clarity and Conciseness：Ensure the conclusion is concise and avoids repeating previously discussed content.
-2.Accuracy： The conclusion should be accurate and remain faithful to the core arguments of the paper.
-3.Academic Rigor：Maintain academic rigor and writing conventions, avoiding excessive generalization or overstatement.
-4.Forward-Looking Perspective：The conclusion should provide forward-looking insights and provide readers with new perspectives or research insights.

Output the conclusion generation result directly.
        """
}
# mapping list
# 将type从str映射到int，现在又从int映射到str，是为了方便后期扩展到多种语言综述的生成
survey_type_int2str = {
    "Chinese": ["技术概念", "方向研究现状", "方法对比分析", "技术方法发展历程"],
    "English": ["an overview of a certain technological concept",
                "the research status of a specific research direction",
                "a comparative analysis and summary of multiple methods",
                "the development history of a certain technical method"]}

generate_outline_samples = [
    {
        'Chinese': [
            """- 1 引言: [123, 45, 87]
- 2 预备知识: [234, 67, 98]
- 3 编码器设计指南: [345, 78, 111, 56]
- 3.1 步骤 1：设计反映高阶交互（HOIs）的特征: [222, 13, 44, 76]
- 3.1.1 外部特征或标签: [187, 32, 46]
- 3.1.2 结构特征: [256, 37, 58]
- 3.2 步骤 2：表达反映高阶交互（HOIs）的超图: [333, 89, 101, 66]
- 3.2.1 简化变换: [167, 28, 39]
- 3.2.2 非简化变换: [278, 41, 52]
- 3.2.3 与图神经网络（GNNs）的比较: [389, 63, 74]
- 3.3 步骤 3：传递反映高阶交互（HOIs）的消息: [444, 91, 103, 82]
- 3.3.1 聚合谁的消息（目标选择）: [198, 23, 36]
- 3.3.2 聚合什么消息（消息表示）: [211, 48, 59]
- 3.3.3 如何聚合消息（聚合函数）: [322, 67, 78]
- 3.3.4 与图神经网络（GNNs）的比较: [433, 89, 90]
- 4 目标设计指南: [555, 112, 124, 65]
- 4.1 分类学习: [266, 135, 47]
- 4.1.1 启发式负采样: [177, 28, 31]
- 4.1.2 可学习的负采样: [299, 42, 53]
- 4.1.3 与图神经网络（GNNs）的比较: [301, 64, 75]
- 4.2 对比学习: [411, 86, 97]
- 4.2.1 视图创建与编码: [156, 27, 38]
- 4.3 生成学习: [522, 107, 118, 49]
- 4.3.1 生成真实的高阶交互（HOIs）: [168, 29, 30]
- 4.3.2 生成潜在的高阶交互（HOIs）: [201, 43, 54]
- 4.3.3 与图神经网络（GNNs）的比较: [311, 66, 77]
- 5 应用指南: [666, 125, 136, 57]
- 5.1 推荐系统: [178, 26, 33]
- 5.2 生物信息学与医学: [288, 45, 56]
- 5.3 时间序列分析: [399, 68, 79]
- 5.4 计算机视觉: [401, 81, 92]
- 6 讨论: [777, 113, 145, 69] """,
            """- 1 引言: [201, 155, 3]
- 1.1 综述结构: [201, 145]
- 2 概念与预备知识: [99, 120, 17]
- 2.1 什么是少样本学习？: [99, 17]
- 2.2 少样本学习与传统机器学习有何关系？: [120, 99]
- 2.3 少样本学习与迁移学习有何关系？: [17, 120]
- 2.4 少样本学习与元学习有何关系？: [17, 99]
- 2.5 数据集: [88, 145]
- 2.6 分类法: [145, 201]
- 3 数据层面：用最大值评估真实数据分布: [145, 88, 25]
- 3.1 数据扩充: [145, 88]
- 3.2 特征增强: [88, 25]
- 3.3 讨论与总结: [25, 145]
- 4 特征层面：为特定问题构建数据到标签的映射: [18, 22, 101]
- 4.1 迁移学习: [18, 22]
- 4.1.1 预训练: [18, 22]
- 4.2 多任务学习: [22, 101]
- 4.3 讨论与总结: [101, 18]
- 5 任务层面：推导目标任务映射的元知识: [133, 5, 101]
- 5.1 学习优化元学习参数: [133, 5]
- 5.2 学习度量算法: [5, 101]
- 5.3 讨论与总结: [101, 5]
- 6 多模态：多模态信息的无损表示: [155, 199]
- 6.1 讨论与总结: [199, 155]
- 7 少样本学习在计算机视觉中的应用: [1, 9, 23]
- 7.1 少样本图像分类: [1, 9]
- 7.2 少样本目标检测: [9, 23]
- 7.3 少样本语义分割: [23, 1]
- 7.4 少样本实例分割: [23, 9]
- 8 少样本学习的未来方向与机遇: [78, 177, 3]
- 8.1 更好地评估数据分布: [78, 3]
- 8.2 提高数据到标签映射的鲁棒性: [177, 155]
- 8.3 更有效地从历史任务中学习元知识: [3, 78]
- 8.4 多模态信息的完全融合: [177, 155]
- 9 结论: [2, 6, 8]
""",
            """- 1 引言: [13, 105, 47]
- 2 大语言模型的训练范式: [23, 128, 89]
- 2.1 传统微调范式: [23, 147, 89]
- 2.2 提示范式: [128, 23, 56]
- 3 传统微调范式的解释: [98, 111, 34]
- 3.1 局部解释: [98, 34, 203]
- 3.2 全局解释: [111, 98, 16]
- 3.2.1 基于探测的解释: [111, 16, 34]
- 3.2.2 神经元激活解释: [16, 203, 98]
- 3.2.3 基于概念的解释: [111, 34, 203]
- 3.2.4 机械可解释性: [203, 16, 111]
- 3.3 利用解释: [34, 98, 155]
- 3.3.1 调试模型: [155, 34, 98]
- 4 提示范式的解释: [77, 132, 27]
- 4.1 基础模型解释: [77, 27, 132]
- 4.1.1 解释上下文学习: [77, 132, 27]
- 4.1.2 解释思维链提示: [132, 77, 27]
- 4.1.3 表示工程: [27, 77, 132]
- 4.2 辅助模型解释: [178, 77, 132]
- 4.2.1 解释微调的作用: [178, 77, 132]
- 4.2.2 解释幻觉现象: [132, 178, 77]
- 4.2.3 不确定性量化: [77, 132, 178]
- 4.3 利用解释: [132, 27, 178]
- 5 解释评估: [43, 189, 62]
- 5.1 传统微调范式中的解释评估: [43, 189, 62]
- 5.2 提示范式中的解释评估: [189, 43, 62]
- 6 研究挑战: [88, 199, 33]
- 6.1 无真实标签的解释: [88, 199, 33]
- 6.2 涌现能力的来源: [199, 88, 33]
- 6.3 比较两种范式: [33, 199, 88]
- 6.4 大语言模型的捷径学习: [88, 33, 199]
- 6.5 注意力冗余: [199, 88, 33]
- 6.6 从快照可解释性转向时间分析: [33, 199, 88]
- 6.7 安全与伦理: [88, 33, 199]
- 7 结论: [12, 166, 59] """],
        'English': [
            """- 1 Introduction: [123, 45, 87]
- 2 Preliminary Knowledge: [234, 67, 98]
- 3 Encoder Design Guidelines: [345, 78, 111, 56]
- 3.1 Step 1: Design Features Reflecting High-Order Interactions (HOIs): [222, 13, 44, 76]
- 3.1.1 External Features or Labels: [187, 32, 46]
- 3.1.2 Structural Features: [256, 37, 58]
- 3.2 Step 2: Express Hypergraphs Reflecting High-Order Interactions (HOIs): [333, 89, 101, 66]
- 3.2.1 Simplified Transformations: [167, 28, 39]
- 3.2.2 Non-Simplified Transformations: [278, 41, 52]
- 3.2.3 Comparison with Graph Neural Networks (GNNs): [389, 63, 74]
- 3.3 Step 3: Transmit Messages Reflecting High-Order Interactions (HOIs): [444, 91, 103, 82]
- 3.3.1 Whose Messages to Aggregate (Target Selection): [198, 23, 36]
- 3.3.2 What Messages to Aggregate (Message Representation): [211, 48, 59]
- 3.3.3 How to Aggregate Messages (Aggregation Function): [322, 67, 78]
- 3.3.4 Comparison with Graph Neural Networks (GNNs): [433, 89, 90]
- 4 Target Design Guidelines: [555, 112, 124, 65]
- 4.1 Classification Learning: [266, 135, 47]
- 4.1.1 Heuristic Negative Sampling: [177, 28, 31]
- 4.1.2 Learnable Negative Sampling: [299, 42, 53]
- 4.1.3 Comparison with Graph Neural Networks (GNNs): [301, 64, 75]
- 4.2 Contrastive Learning: [411, 86, 97]
- 4.2.1 View Creation and Encoding: [156, 27, 38]
- 4.3 Generative Learning: [522, 107, 118, 49]
- 4.3.1 Generate Real High-Order Interactions (HOIs): [168, 29, 30]
- 4.3.2 Generate Potential High-Order Interactions (HOIs): [201, 43, 54]
- 4.3.3 Comparison with Graph Neural Networks (GNNs): [311, 66, 77]
- 5 Application Guidelines: [666, 125, 136, 57]
- 5.1 Recommendation Systems: [178, 26, 33]
- 5.2 Bioinformatics and Medicine: [288, 45, 56]
- 5.3 Time Series Analysis: [399, 68, 79]
- 5.4 Computer Vision: [401, 81, 92]
- 6 Discussion: [777, 113, 145, 69]""",
            """-1 Introduction: [12, 45, 78]
-1.1 Overview structure: [67, 35]
-2 Concepts and prerequisites: [56, 89, 23]
-2.1 What is few-shot learning? : [14, 67]
-2.2 How is few-shot learning related to traditional machine learning? : [56, 89]
-2.3 How is few-shot learning related to transfer learning? : [98, 42]
-2.4 How is few-shot learning related to meta-learning? : [21, 75, 64]
-2.5 Datasets: [11, 53, 86]
-2.6 Classification: [18, 34, 59]
-3 Data level: Using the maximum value to estimate the true data distribution: [45, 76, 99]
-3.1 Data augmentation: [22, 64]
-3.2 Feature enhancement: [18, 53, 77]
-3.3 Discussion and summary: [65, 41]
-4 Feature level: Constructing a mapping from data to labels for a specific problem: [34, 28, 79]
-4.1 Transfer learning: [44, 37, 66]
-4.1.1 Pre-training: [90, 12, 53]
-4.2 Multi-task learning: [13, 82, 21]
-4.3 Discussion and summary: [54, 49]
-5 Task level: deriving meta-knowledge of target-task mapping: [70, 25, 58]
-5.1 Learning to optimize meta-learning parameters: [9, 64, 88]
-5.2 Learning metric algorithms: [71, 13, 40]
-5.3 Discussion and summary: [62, 93, 27]
-6 Multimodality: Lossless representation of multimodal information: [73, 45]
-6.1 Discussion and summary: [39, 56]
-7 Applications of few-shot learning in computer vision: [91, 83, 52]
-7.1 Few-shot image classification: [49, 54, 35]
-7.2 Few-shot object detection: [22, 13, 64]
-7.3 Few-shot semantic segmentation: [17, 42, 76]
-7.4 Few-shot instance segmentation: [58, 61, 74]
-8 Future directions and opportunities for few-shot learning: [84, 50, 29]
-8.1 Better estimate of data distribution: [31, 43, 78]
-8.2 Improve the robustness of data-to-label mapping: [60, 83]
-8.3 More effectively learn meta-knowledge from historical tasks: [9, 57, 36]
-8.4 Full integration of multimodal information: [18, 92, 53]
-9 Conclusion: [68, 54]
""",
            """-1 Introduction: [62, 9, 101]
-2 Training paradigms for large language models: [28, 45, 76]
-2.1 Traditional fine-tuning paradigm: [112, 54, 43]
-2.2 Prompt paradigm: [89, 34, 25]
-3 Explanation of traditional fine-tuning paradigm: [102, 56, 91]
-3.1 Local explanation: [76, 68, 113]
-3.2 Global explanation: [43, 77, 112]
-3.2.1 Probe-based explanation: [110, 22]
-3.2.2 Neuron activation explanation: [33, 121, 97]
-3.2.3 Concept-based explanation: [98, 54, 18]
-3.2.4 Mechanical interpretability: [65, 91]
-3.3 Using explanations: [82, 99]
-3.3.1 Debugging models: [46, 11]
-4 Explanations of suggested models: [66, 72, 31]
-4.1 Explanations of basic models: [27, 48, 93]
-4.1.1 Explaining contextual learning: [74, 105]
-4.1.2 Explaining chaining cues: [23, 53, 100]
-4.1.3 Representation engineering: [56, 77, 118]
-4.2 Explanations of auxiliary models: [109, 44, 119]
-4.2.1 Explaining the role of fine-tuning: [120, 88]
-4.2.2 Explaining hallucinations: [62, 36]
-4.2.3 Uncertainty quantification: [55, 47, 107]
-4.3 Leveraging explanations: [65, 108, 59]
-5 Explanation evaluation: [81, 105, 61]
-5.1 Explanation evaluation in the traditional fine-tuning paradigm: [74, 33]
-5.2 Explanation evaluation in the prompting paradigm: [112, 49]
-6 Research challenges: [116, 70, 92, 60]
-6.1 Explanations without ground truth labels: [117, 103]
-6.2 Sources of emergent power: [84, 110, 41]
-6.3 Comparing the two paradigms: [113, 100]
-6.4 Shortcut learning for large language models: [67, 90, 104]
-6.5 Attention redundancy: [32, 87, 119]
-6.6 Moving from snapshot interpretability to temporal analysis: [80, 120]
-6.7 Safety and Ethics: [63, 118, 121]
-7 Conclusion: [109, 78, 65]
"""]},
    {
        'Chinese': [
            """-  1 引言: [102, 87, 34]
-  1.1 本文的贡献: [56, 13, 98]
-  1.2 组织结构: [74, 21]
-  2 稀有事件数据: [119, 43, 78]
-  2.1 稀有事件数据集 – 现有稀有事件数据集的分析: [32, 145, 67]
-  2.1.1 稀有程度: [23, 109]
-  2.1.2 行业和实际应用: [98, 47, 156]
-  2.1.3 稀有事件数据集的类型: [63, 88, 150]
-  2.1.4 稀有事件元数据: [101, 76]
-  2.1.5 稀有事件数据集的特征及相关挑战: [95, 28, 142]
-  2.1.6 导致数据集稀有性的因素: [132, 45]
-  3 数据处理方法: [91, 103, 54]
-  3.1 数据处理方法的目标: [110, 71]
-  3.2 数据清洗 (DC): [92, 57, 33]
-  3.2.1 数据清洗方法: [120, 86, 140]
-  3.3 特征选择 (FS): [73, 114, 62]
-  3.3.1 特征选择方法: [41, 99, 149]
-  3.4 采样 (SL): [105, 25]
-  3.4.1 采样方法: [48, 133, 161]
-  3.5 特征工程 (FE): [68, 150, 37]
-  3.5.1 特征工程方法: [55, 126]
-  4 算法方法: [84, 39, 122]
-  4.1 算法方法的重要性: [117, 46]
-  4.2 监督分类和回归: [131, 82, 29]
-  4.2.1 阈值方法: [140, 90]
-  4.2.2 基于树的分类方法: [104, 121]
-  4.2.3 成本敏感学习: [107, 77]
-  4.2.4 非参数分类算法: [66, 137, 49]
-  4.2.5 基于核的方法: [108, 151, 41]
-  4.2.6 推理/基于规则的方法: [123, 36]
-  4.3 半监督和无监督方法: [139, 30, 88]
-  4.3.1 聚类方法: [128, 72, 47]
-  4.3.2 单类学习: [111, 152]
-  4.4 统计建模: [157, 83, 96]
-  4.5 元启发式优化: [138, 59]
-  4.6 高级学习方法: [144, 70, 26]
-  4.6.1 基于注意力的机制: [125, 63]
-  4.6.2 马尔可夫方法: [146, 97, 113]
-  4.6.3 主动学习: [153, 79]
-  4.6.4 元学习: [143, 61, 55]
-  4.7 算法方法的比较: [148, 35]
-  5 评估方法: [50, 141, 116]
-  5.1 评估的重要性: [127, 94]
-  5.2 评估方法: [134, 89, 20]
-  5.2.1 通用评估方法: [147, 112]
-  5.2.2 稀有事件特定的评估方法: [118, 86, 44]
-  5.3 性能指标: [100, 130, 156]
-  6 研究发现与讨论: [103, 69, 60]
-  6.1 当前文献中的空白: [65, 85]
-  6.2 稀有事件预测的开放挑战: [160, 53, 58]
-  6.3 稀有事件预测的研究趋势: [93, 31]
-  7 结论: [158, 81, 42]
""",
            """-1 引言: [135, 42, 97]
-1.1 为什么文本水印对大语言模型有益？: [154, 23, 87]
-1.2 为什么大语言模型对文本水印有益？: [46, 108]
-1.3 为什么需要大语言模型时代的文本水印调查？: [119, 64, 153]
-2 文本水印的基础: [99, 76, 182]
-2.1 文本水印算法: [55, 143, 101]
-2.2 与相关概念的联系: [167, 82]
-2.3 文本水印算法的关键特征: [195, 73, 124]
-2.4 文本水印算法的分类: [149, 33, 172]
-3 现有文本的水印: [121, 95, 186]
-3.1 基于格式的水印: [140, 112, 66]
-3.2 基于词汇的水印: [84, 178]
-3.3 基于句法的水印: [51, 199, 137]
-3.4 基于生成的水印: [161, 89]
-4 大语言模型的水印: [110, 62, 181]
-4.1 在Logits生成过程中添加水印: [145, 126, 90]
-4.1.1 增强水印可检测性: [138, 102]
-4.1.2 减轻对文本质量的影响: [191, 78]
-4.1.3 扩展水印容量: [165, 49, 92]
-4.2 在Token采样过程中添加水印: [176, 57, 130]
-4.2.1 Token级采样水印: [177, 83]
-4.3 在大语言模型训练过程中添加水印: [180, 100, 59]
-4.3.1 基于触发器的水印: [113, 144, 37]
-4.3.2 全局水印: [162, 107]
-5 文本水印的评估指标: [94, 115, 185]
-5.1 可检测性: [141, 171]
-5.1.1 零比特水印: [192, 61]
-5.1.2 多比特水印: [132, 87, 160]
-5.1.3 水印大小: [147, 168]
-5.2 水印文本的质量影响: [58, 111]
-5.2.1 比较评估指标: [174, 104, 138]
-5.3 水印大语言模型的输出性能评估: [103, 69, 151]
-5.3.1 文本补全: [182, 125]
-5.3.2 代码生成: [98, 129, 190]
-5.3.3 其他任务: [183, 45]
-5.4 水印大语言模型的输出多样性评估: [148, 91]
-5.5 非定向水印攻击: [136, 166, 127]
-5.5.1 威胁模型: [109, 142]
-5.5.2 字符级攻击: [134, 53, 170]
-5.5.3 对现有文本的词汇级攻击: [198, 159]
-5.5.4 文本生成过程中的词汇级攻击: [105, 75]
-5.5.5 改写攻击: [96, 193, 88]
-5.5.6 复制粘贴攻击: [155, 50]
-5.5.7 其他文档级攻击: [152, 184, 80]
-5.6 定向水印攻击: [200, 71, 56]
-5.6.1 威胁模型: [139, 169]
-5.6.2 针对KGW的定向水印攻击: [187, 163, 77]
-5.6.3 水印蒸馏: [193, 128]
-5.7 基准测试和工具: [146, 70]
-6 文本水印的应用: [131, 179, 67]
-6.1 版权保护: [88, 175]
-6.1.1 文本版权: [164, 48, 156]
-6.1.2 数据集版权: [106, 118]
-6.1.3 大语言模型版权: [157, 197, 85]
-6.2 AI生成文本检测: [120, 52]
-7 挑战与未来方向: [189, 116, 79]
-7.1 文本水印算法设计中的挑战性权衡: [72, 86, 194]
-7.1.1 水印大小、非定向攻击下的鲁棒性和水印容量: [93, 188]
-7.1.2 非定向和定向攻击下的鲁棒性: [150, 146]
-7.1.3 多样性和非定向攻击下的鲁棒性: [178, 99]
-7.1.4 定向攻击和模型提取攻击下的鲁棒性: [123, 117]
-7.1.5 文本质量和非定向攻击下的鲁棒性: [126, 158]
-7.2 文本水印算法的挑战性场景: [133, 81]
-7.2.1 低熵场景: [97, 114]
-7.2.2 公开可验证场景: [122, 173]
-7.2.3 开源场景: [185, 177]
-7.3 无额外负担的水印应用挑战: [153, 74]
-8 结论: [107, 151, 154]
""",
            """-  1 引言: [135, 42, 97]
-  2 分类: [154, 23, 87]
-  2.1 符号: [46, 108]
-  2.2 提出的两级分类: [119, 64, 153]
-  2.2.1 第一级：推荐场景的分类: [99, 76, 182]
-  2.2.2 第二级：推荐任务的分类: [55, 143, 101]
-  2.3 不同推荐场景下的基于方法的分类: [167, 82]
-  3 场景1：用户不重叠和项目不重叠: [195, 73, 124]
-  3.1 提取集群级评分模式: [149, 33, 172]
-  3.1.1 基本范式: [121, 95, 186]
-  3.1.2 该方法的方法: [140, 112, 66]
-  3.1.3 扩展到多目标推荐: [84, 178]
-  3.2 捕捉标签相关性: [51, 199, 137]
-  3.2.1 基本范式: [161, 89]
-  3.2.2 该方法的方法: [110, 62, 181]
-  3.2.3 扩展到跨域推荐: [145, 126, 90]
-  3.3 应用主动学习: [138, 102]
-  3.3.1 基本范式: [191, 78]
-  3.3.2 该方法的方法: [165, 49, 92]
-  3.4 讨论: [176, 57, 130]
-  4 场景2：用户部分重叠和项目不重叠: [177, 83]
-  4.1 集体矩阵分解: [180, 100, 59]
-  4.1.1 基本范式: [113, 144, 37]
-  4.1.2 该方法的方法: [162, 107]
-  4.2 重叠用户的表示组合: [94, 115, 185]
-  4.2.1 基本范式: [141, 171]
-  4.2.2 该方法的方法: [192, 61]
-  4.3 嵌入和映射: [132, 87, 160]
-  4.3.1 基本范式: [147, 168]
-  4.3.2 该方法的方法: [58, 111]
-  4.3.3 扩展到域内推荐: [174, 104, 138]
-  4.4 基于图神经网络的方法: [103, 69, 151]
-  4.4.1 基本范式: [182, 125]
-  4.4.2 该方法的方法: [98, 129, 190]
-  4.5 捕捉方面相关性: [183, 45]
-  4.5.1 基本范式: [148, 91]
-  4.5.2 该方法的方法: [136, 166, 127]
-  4.6 讨论: [109, 142]
-  5 场景3：用户完全重叠和项目不重叠: [134, 53, 170]
-  5.1 集体矩阵分解: [198, 159]
-  5.1.1 基本范式: [105, 75]
-  5.1.2 该方法的方法: [96, 193, 88]
-  5.2 张量分解: [155, 50]
-  5.2.1 基本范式: [152, 184, 80]
-  5.2.2 该方法的方法: [200, 71, 56]
-  5.3 因子分解机: [139, 169]
-  5.3.1 基本范式: [187, 163, 77]
-  5.3.2 该方法的方法: [193, 128]
-  5.4 深度共享用户表示: [146, 70]
-  5.4.1 基本范式: [131, 179, 67]
-  5.4.2 该方法的方法: [88, 175]
-  5.5 深度双知识转移: [164, 48, 156]
-  5.5.1 基本范式: [106, 118]
-  5.5.2 该方法的方法: [157, 197, 85]
-  5.6 深度整合源域信息: [120, 52]
-  5.6.1 基本范式: [189, 116, 79]
-  5.6.2 该方法的方法: [72, 86, 194]
-  5.7 讨论: [93, 188]
-  6 跨域推荐的数据集: [150, 146]
-  6.1 多域数据集: [178, 99]
-  6.2 单域数据集: [123, 117]
-  6.2.1 电影: [126, 158]
·  6.2.2 书籍: [133, 81]
·  6.2.3 音乐: [97, 114]
-  7 未来方向与挑战: [122, 173]
-  7.1 探索未研究的推荐场景: [185, 177]
-  7.2 采用深度学习的最新进展: [153, 74]
-  7.3 探索推荐的鲁棒性: [107, 151, 154]
-  7.4 深度跨域推荐模型的可扩展性: [175, 83, 141]
-  7.5 跨域推荐的可解释性: [90, 121, 167]
-  8 结论: [108, 99, 182]"""],
        'English': [
            """-1 Introduction: [102, 87, 34]
-1.1 Contributions of this paper: [56, 13, 98]
-1.2 Structure of the paper: [74, 21]
-2 Rare Event Data: [119, 43, 78]
-2.1 Rare Event Datasets – Analysis of Existing Rare Event Datasets: [32, 145, 67]
-2.1.1 Rarity: [23, 109]
-2.1.2 Industry and Practical Applications: [98, 47, 156]
-2.1.3 Types of Rare Event Datasets: [63, 88, 150]
-2.1.4 Rare Event Metadata: [101, 76]
-2.1.5 Characteristics and Associated Challenges of Rare Event Datasets: [95, 28, 142]
-2.1.6 Factors Leading to Dataset Rarity: [132, 45]
-3 Data Processing Methods: [91, 103, 54]
-3.1 Objectives of Data Processing Methods: [110, 71]
-3.2 Data Cleaning (DC): [92, 57, 33]
-3.2.1 Data Cleaning Methods: [120, 86, 140]
-3.3 Feature Selection (FS): [73, 114, 62]
-3.3.1 Feature Selection Methods: [41, 99, 149]
-3.4 Sampling (SL): [105, 25]
-3.4.1 Sampling Methods: [48, 133, 161]
-3.5 Feature Engineering (FE): [68, 150, 37]
-3.5.1 Feature Engineering Methods: [55, 126]
-4 Algorithmic Methods: [84, 39, 122]
-4.1 Importance of Algorithmic Methods: [117, 46]
-4.2 Supervised Classification and Regression: [131, 82, 29]
-4.2.1 Threshold Methods: [140, 90]
-4.2.2 Tree-based Classification Methods: [104, 121]
-4.2.3 Cost-sensitive Learning: [107, 77]
-4.2.4 Non-parametric Classification Algorithms: [66, 137, 49]
-4.2.5 Kernel-based Methods: [108, 151, 41]
-4.2.6 Inference/Rule-based Methods: [123, 36]
-4.3 Semi-supervised and Unsupervised Methods: [139, 30, 88]
-4.3.1 Clustering Methods: [128, 72, 47]
-4.3.2 One-class Learning: [111, 152]
-4.4 Statistical Modeling: [157, 83, 96]
-4.5 Meta-heuristic Optimization: [138, 59]
-4.6 Advanced Learning Methods: [144, 70, 26]
-4.6.1 Attention-based Mechanisms: [125, 63]
-4.6.2 Markov Methods: [146, 97, 113]
-4.6.3 Active Learning: [153, 79]
-4.6.4 Meta-learning: [143, 61, 55]
-4.7 Comparison of Algorithmic Methods: [148, 35]
-5 Evaluation Methods: [50, 141, 116]
-5.1 Importance of Evaluation: [127, 94]
-5.2 Evaluation Methods: [134, 89, 20]
-5.2.1 General Evaluation Methods: [147, 112]
-5.2.2 Rare Event Specific Evaluation Methods: [118, 86, 44]
-5.3 Performance Metrics: [100, 130, 156]
-6 Findings and Discussion: [103, 69, 60]
-6.1 Gaps in Current Literature: [65, 85]
-6.2 Open Challenges in Rare Event Prediction: [160, 53, 58]
-6.3 Research Trends in Rare Event Prediction: [93, 31]
-7 Conclusion: [158, 81, 42]
-8 Abbreviations: [155, 124]
""",
            """-1 Introduction: [135, 42, 97]
-1.1 Why are Text Watermarks Beneficial for Large Language Models?: [154, 23, 87]
-1.2 Why are Large Language Models Beneficial for Text Watermarks?: [46, 108]
-1.3 Why is a Survey of Text Watermarks in the Era of Large Language Models Needed?: [119, 64, 153]
-2 Fundamentals of Text Watermarks: [99, 76, 182]
-2.1 Text Watermarking Algorithms: [55, 143, 101]
-2.2 Connection to Related Concepts: [167, 82]
-2.3 Key Features of Text Watermarking Algorithms: [195, 73, 124]
-2.4 Classification of Text Watermarking Algorithms: [149, 33, 172]
-3 Existing Text Watermarks: [121, 95, 186]
-3.1 Format-based Watermarks: [140, 112, 66]
-3.2 Vocabulary-based Watermarks: [84, 178]
-3.3 Syntax-based Watermarks: [51, 199, 137]
-3.4 Generation-based Watermarks: [161, 89]
-4 Watermarking in Large Language Models: [110, 62, 181]
-4.1 Adding Watermarks in the Logits Generation Process: [145, 126, 90]
-4.1.1 Enhancing Watermark Detectability: [138, 102]
-4.1.2 Mitigating Impact on Text Quality: [191, 78]
-4.1.3 Expanding Watermark Capacity: [165, 49, 92]
-4.2 Adding Watermarks in Token Sampling Process: [176, 57, 130]
-4.2.1 Token-level Sampling Watermarks: [177, 83]
-4.3 Adding Watermarks in Large Language Model Training Process: [180, 100, 59]
-4.3.1 Trigger-based Watermarks: [113, 144, 37]
-4.3.2 Global Watermarks: [162, 107]
-5 Evaluation Metrics for Text Watermarks: [94, 115, 185]
-5.1 Detectability: [141, 171]
-5.1.1 Zero-bit Watermarks: [192, 61]
-5.1.2 Multi-bit Watermarks: [132, 87, 160]
-5.1.3 Watermark Size: [147, 168]
-5.2 Impact on Quality of Watermarked Text: [58, 111]
-5.2.1 Comparative Evaluation Metrics: [174, 104, 138]
-5.3 Evaluation of Output Performance in Watermarked Large Language Models: [103, 69, 151]
-5.3.1 Text Completion: [182, 125]
-5.3.2 Code Generation: [98, 129, 190]
-5.3.3 Other Tasks: [183, 45]
-5.4 Evaluation of Output Diversity in Watermarked Large Language Models: [148, 91]
-5.5 Non-targeted Watermark Attacks: [136, 166, 127]
-5.5.1 Threat Models: [109, 142]
-5.5.2 Character-level Attacks: [134, 53, 170]
-5.5.3 Vocabulary-level Attacks on Existing Texts: [198, 159]
-5.5.4 Vocabulary-level Attacks in Text Generation Process: [105, 75]
-5.5.5 Rewriting Attacks: [96, 193, 88]
-5.5.6 Copy-paste Attacks: [155, 50]
-5.5.7 Other Document-level Attacks: [152, 184, 80]
-5.6 Targeted Watermark Attacks: [200, 71, 56]
-5.6.1 Threat Models: [139, 169]
-5.6.2 Targeted Watermark Attacks on KGW: [187, 163, 77]
-5.6.3 Watermark Distillation: [193, 128]
-5.7 Benchmarking and Tools: [146, 70]
-6 Applications of Text Watermarks: [131, 179, 67]
-6.1 Copyright Protection: [88, 175]
-6.1.1 Text Copyright: [164, 48, 156]
-6.1.2 Dataset Copyright: [106, 118]
-6.1.3 Large Language Model Copyright: [157, 197, 85]
-6.2 AI-generated Text Detection: [120, 52]
-7 Challenges and Future Directions: [189, 116, 79]
-7.1 Challenging Trade-offs in Text Watermark Algorithm Design: [72, 86, 194]
-7.1.1 Watermark Size, Robustness under Non-targeted Attacks, and Watermark Capacity: [93, 188]
-7.1.2 Robustness under Non-targeted and Targeted Attacks: [150, 146]
-7.1.3 Robustness under Diversity and Non-targeted Attacks: [178, 99]
-7.1.4 Robustness under Targeted Attacks and Model Extraction Attacks: [123, 117]
-7.1.5 Robustness under Text Quality and Non-targeted Attacks: [126, 158]
-7.2 Challenging Scenarios for Text Watermark Algorithms: [133, 81]
-7.2.1 Low-entropy Scenarios: [97, 114]
-7.2.2 Publicly Verifiable Scenarios: [122, 173]
-7.2.3 Open-source Scenarios: [185, 177]
-7.3 Challenges in Watermark Application without Extra Burden: [153, 74]
-8 Conclusion: [107, 151, 154]""",
            """- 1 Introduction: [135, 42, 97]
- 2 Classification: [154, 23, 87]
- 2.1 Notation: [46, 108]
- 2.2 Proposed two-level classification: [119, 64, 153]
- 2.2.1 First level: classification of recommendation scenarios: [99, 76, 182]
- 2.2.2 Second level: classification of recommendation tasks: [55, 143, 101]
- 2.3 Method-based classification under different recommendation scenarios: [167, 82]
- 3 Scenario 1: Non-overlapping users and non-overlapping items: [195, 73, 124]
- 3.1 Extracting cluster-level rating patterns: [149, 33, 172]
- 3.1.1 Basic paradigm: [121, 95, 186]
- 3.1.2 The approach of this method: [140, 112, 66]
- 3.1.3 Extension to multi-target recommendation: [84, 178]
- 3.2 Capturing label correlation: [51, 199, 137]
- 3.2.1 Basic paradigm: [161, 89]
- 3.2.2 The approach of this method: [110, 62, 181]
- 3.2.3 Extension to cross-domain recommendation: [145, 126, 90]
- 3.3 Applying active learning: [138, 102]
- 3.3.1 Basic paradigm: [191, 78]
- 3.3.2 The approach of this method: [165, 49, 92]
- 3.4 Discussion: [176, 57, 130]
- 4 Scenario 2: Partially overlapping users and non-overlapping items: [177, 83]
- 4.1 Collective matrix factorization: [180, 100, 59]
- 4.1.1 Basic paradigm: [113, 144, 37]
- 4.1.2 Method of this method: [162, 107]
- 4.2 Representation combination of overlapping users: [94, 115, 185]
- 4.2.1 Basic paradigm: [141, 171]
- 4.2.2 Method of this method: [192, 61]
- 4.3 Embedding and mapping: [132, 87, 160]
- 4.3.1 Basic paradigm: [147, 168]
- 4.3.2 Method of this method: [58, 111]
- 4.3.3 Extending to intra-domain recommendations: [174, 104, 138]
- 4.4 Graph neural network-based approaches: [103, 69, 151]
- 4.4.1 Basic paradigm: [182, 125]
- 4.4.2 Approaches to this approach: [98, 129, 190]
- 4.5 Capturing aspect correlations: [183, 45]
- 4.5.1 Basic paradigm: [148, 91]
- 4.5.2 Approaches to this approach: [136, 166, 127]
- 4.6 Discussion: [109, 142]
- 5 Scenario 3: Complete user overlap and non-overlapping items: [134, 53, 170]
- 5.1 Collective matrix factorization: [198, 159]
- 5.1.1 Basic Form: [105, 75]
- 5.1.2 Methods of this method: [96, 193, 88]
- 5.2 Tensor decomposition: [155, 50]
- 5.2.1 Basic Form: [152, 184, 80]
- 5.2.2 Methods of this method: [200, 71, 56]
- 5.3 Factorization Machine: [139, 169]
- 5.3.1 Basic Form: [187, 163, 77]
- 5.3.2 Methods of this method: [193, 128]
- 5.4 Deep Shared User Representation: [146, 70]
- 5.4.1 Basic Form: [131, 179, 67]
- 5.4.2 Methods of this method: [88, 175]
- 5.5 Deep Dual Knowledge Transfer: [164, 48, 156]
- 5.5.1 Basic Paradigm: [106, 118]
- 5.5.2 Method of this Method: [157, 197, 85]
- 5.6 Deep Integration of Source Domain Information: [120, 52]
- 5.6.1 Basic Paradigm: [189, 116, 79]
- 5.6.2 Method of this Method: [72, 86, 194]
- 5.7 Discussion: [93, 188]
- 6 Datasets for Cross-Domain Recommendation: [150, 146]
- 6.1 Multi-Domain Datasets: [178, 99]
- 6.2 Single-Domain Datasets: [123, 117]
- 6.2.1 Movies: [126, 158]
· 6.2.2 Books: [133, 81]
· 6.2.3 Music: [97, 114]
- 7 Future Directions and Challenges: [122, 173]
- 7.1 Exploring Unexplored Recommendation Scenarios: [185, 177]
- 7.2 Recent Advances in Deep Learning: [153, 74]
- 7.3 Exploring Robustness of Recommendations: [107, 151, 154]
- 7.4 Scalability of Deep Cross-Domain Recommendation Models: [175, 83, 141]
- 7.5 Explainability of Cross-Domain Recommendations: [90, 121, 167]
- 8 Conclusion: [108, 99, 182]"""]},
    {
        'Chinese': [
            """-  1 引言: [145, 32, 98]
-  1.1 Transformer: [176, 54, 23]
-  1.2 轻量级和快速Transformer: [121, 89, 187]
-  2 Transformer: [142, 36, 75]
-  2.1 注意力机制: [178, 47, 156]
-  2.2 编码器: [199, 68, 103]
-  2.3 解码器: [57, 189, 112]
-  2.4 复杂度: [91, 161, 83]
-  3 通用方法: [134, 149, 66]
-  4 专用方法: [153, 48, 173]
-  4.1 稀疏注意力: [122, 194, 101]
-  4.2 因式分解注意力: [116, 77, 137]
-  4.3 架构变更: [186, 53, 97]
-  5 缺点: [182, 72, 168]
-  6 高效Transformer的更广泛影响: [139, 93, 155]
-  7 未来研究方向: [104, 162, 200]
-  7.1 效率和可承受性: [127, 95, 188]
-  7.2 泛化性能: [163, 85, 174]
-  8 结论: [192, 140, 79]""",
            """-1 引言: [145, 32, 98]
-2 预备知识: [176, 54, 23]
-2.1 多视图数据: [121, 89, 187]
-2.2 问题定义: [142, 36, 75]
-2.3 与多视图聚类相关的原则: [178, 47, 156]
-2.4 信息融合策略: [199, 68, 103]
-2.5 聚类流程: [57, 189, 112]
-2.6 加权策略: [91, 161, 83]
-2.7 模型结构: [134, 149, 66]
-2.8 优化方案: [153, 48, 173]
-2.9 提出的分类法: [122, 194, 101]
-3 完整多视图聚类: [116, 77, 137]
-3.1 基于非负矩阵分解（NMF）的多视图聚类方法: [186, 53, 97]
-3.2 基于多核学习（MKL）的多视图聚类方法: [182, 72, 168]
-3.3 基于图的多视图聚类方法: [139, 93, 155]
-3.4 基于子空间的多视图聚类方法: [104, 162, 200]
-3.5 基于深度学习的多视图聚类方法: [127, 95, 188]
-3.6 基于对比学习的多视图聚类方法: [163, 85, 174]
-3.7 基于协同学习的多视图聚类方法: [192, 140, 79]
-3.8 基于自定进度学习的多视图聚类方法: [176, 99, 184]
-3.9 讨论: [132, 56, 197]
-4 不完整多视图聚类: [154, 182, 70]
-4.1 基于非负矩阵分解（NMF）的不完整多视图聚类方法: [145, 88, 199]
-4.2 基于多核学习（MKL）的不完整多视图聚类方法: [109, 173, 198]
-4.3 基于图的不完整多视图聚类方法: [161, 147, 83]
-4.4 基于子空间的不完整多视图聚类方法: [121, 99, 174]
-4.5 基于深度学习的不完整多视图聚类方法: [157, 64, 193]
-4.6 基于对比学习的不完整多视图聚类方法: [188, 53, 172]
-4.7 讨论: [190, 105, 169]
-5 不确定多视图聚类: [151, 77, 137]
-6 动态多视图聚类: [173, 58, 112]
-7 数据集: [126, 108, 195]
-7.1 文本数据集: [144, 92, 187]
-7.2 图像数据集: [178, 163, 120]
-7.3 文本 - 基因数据集: [191, 85, 152]
-7.4 图像 - 文本数据集: [167, 56, 138]
-7.5 视频数据集: [129, 99, 184]
-8 性能指标: [136, 111, 149]
-8.1 内部指标: [154, 87, 193]
-8.2 外部指标: [197, 101, 165]
-9 实证评估: [159, 88, 130]
-10 未来工作: [183, 74, 122]
-11 结论: [175, 109, 141]""",
            """-1 引言: [145, 32, 98]
-2 概述: [176, 54, 23]
-2.1 聚类: [121, 89, 187]
-2.2 算法选择: [142, 36, 75]
-2.2.1 问题概述: [178, 47, 156]
-2.2.2 用于算法选择的元学习: [199, 68, 103]
-2.3 超参数调优: [57, 189, 112]
-2.3.1 问题概述: [91, 161, 83]
-2.3.2 超参数调优的优化方法: [134, 149, 66]
-3 分类法: [153, 48, 173]
-3.1 算法选择: [122, 194, 101]
-3.2 超参数调优: [116, 77, 137]
-4 将自动机器学习系统映射到分类法: [186, 53, 97]
-4.1 算法选择方法: [182, 72, 168]
-4.1.1 利用真实标签的工作: [139, 93, 155]
-4.1.2 基于内部聚类有效性指标的工作: [104, 162, 200]
-4.2 用于聚类的自动机器学习系统: [127, 95, 188]
-5 比较: [163, 85, 174]
-6 开放挑战: [192, 140, 79]
-6.1 聚类有效性指标选择: [176, 99, 184]
-6.2 特定于聚类的元特征: [132, 56, 197]
-6.3 大数据自动机器学习: [154, 182, 70]
-6.4 多视图和子空间聚类: [145, 88, 199]
-7 结论: [175, 109, 141]"""],
        'English': [
            """- 1 Introduction
- 1.1 Transformer
- 1.2 Lightweight and fast Transformer
- 2 Transformer
- 2.1 Attention
- 2.2 Encoder
- 2.3 Decoder
- 2.4 Complexity
- 3 General methods
- 4 Special methods
- 4.1 Sparse attention
- 4.2 Factorized Attention
- 4.3 Architectural Changes
- 5 Disadvantages
- 6 The Broader Impact of Efficient Transformers
- 7 Future Research Directions
- 7.1 Efficiency and Affordability
- 7.2 Generalization Performance
- 8 Conclusion""",
            """-1 Introduction: [145, 32, 98]
-2 Preliminary knowledge: [176, 54, 23]
-2.1 Multi-view data: [121, 89, 187]
-2.2 Problem definition: [142, 36, 75]
-2.3 Principles related to multi-view clustering: [178, 47, 156]
-2.4 Information fusion strategy: [199, 68, 103]
-2.5 Clustering process: [57, 189, 112]
-2.6 Weighting strategy: [91, 161, 83]
-2.7 Model structure: [134, 149, 66]
-2.8 Optimization scheme: [153, 48, 173]
-2.9 Proposed classification method: [122, 194, 101]
-3 Complete multi-view clustering: [116, 77, 137]
-3.1 Multi-view clustering method based on non-negative matrix factorization (NMF): [186, 53, 97]
-3.2 Multi-view clustering method based on multi-kernel learning (MKL): [182, 72, 168]
-3.3 Multi-view clustering method based on graph: [139, 93, 155]
-3.4 Multi-view clustering method based on subspace: [104, 162, 200]
-3.5 Multi-view clustering method based on deep learning: [127, 95, 188]
-3.6 Multi-view clustering method based on contrastive learning: [163, 85, 174]
-3.7 Multi-view clustering method based on collaborative learning: [192, 140, 79]
-3.8 Multi-view clustering based on self-paced learning: [176, 99, 184]
-3.9 Discussion: [132, 56, 197]
-4 Incomplete multi-view clustering: [154, 182, 70]
-4.1 Incomplete multi-view clustering based on non-negative matrix factorization (NMF): [145, 88, 199]
-4.2 Incomplete multi-view clustering based on multiple kernel learning (MKL): [109, 173, 198]
-4.3 Incomplete multi-view clustering based on graph: [161, 147, 83]
-4.4 Incomplete multi-view clustering based on subspace: [121, 99, 174]
-4.5 Incomplete multi-view clustering based on deep learning: [157, 64, 193]
-4.6 Incomplete multi-view clustering based on contrastive learning: [188, 53, 172]
-4.7 Discussion: [190, 105, 169]
-5 Uncertain multi-view clustering: [151, 77, 137]
-6 Dynamic multi-view clustering: [173, 58, 112]
-7 Datasets: [126, 108, 195]
-7.1 Text datasets: [144, 92, 187]
-7.2 Image datasets: [178, 163, 120]
-7.3 Text-gene datasets: [191, 85, 152]
-7.4 Image-text datasets: [167, 56, 138]
-7.5 Video datasets: [129, 99, 184]
-8 Performance indicators: [136, 111, 149]
-8.1 Internal indicators: [154, 87, 193]
-8.2 External indicators: [197, 101, 165]
-9 Empirical evaluation: [159, 88, 130]
-10 Future work: [183, 74, 122]
-11 Conclusion: [175, 109, 141]""",
            """-1 Introduction: [145, 32, 98]
-2 Overview: [176, 54, 23]
-2.1 Clustering: [121, 89, 187]
-2.2 Algorithm selection: [142, 36, 75]
-2.2.1 Problem overview: [178, 47, 156]
-2.2.2 Meta-learning for algorithm selection: [199, 68, 103]
-2.3 Hyperparameter tuning: [57, 189, 112]
-2.3.1 Problem overview: [91, 161, 83]
-2.3.2 Optimization methods for hyperparameter tuning: [134, 149, 66]
-3 Classification: [153, 48, 173]
-3.1 Algorithm selection: [122, 194, 101]
-3.2 Hyperparameter tuning: [116, 77, 137]
-4 Mapping automated machine learning systems to taxonomies: [186, 53, 97]
-4.1 Methods for algorithm selection: [182, 72, 168]
-4.1.1 Work using true labels: [139, 93, 155]
-4.1.2 Work based on internal clustering validity metrics: [104, 162, 200]
-4.2 Automated machine learning systems for clustering: [127, 95, 188]
-5 Comparisons: [163, 85, 174]
-6 Open challenges: [192, 140, 79]
-6.1 Clustering validity metric selection: [176, 99, 184]
-6.2 Clustering-specific meta-features: [132, 56, 197]
-6.3 Automatic machine learning on big data: [154, 182, 70]
-6.4 Multi-view and subspace clustering: [145, 88, 199]
-7 Conclusion: [175, 109, 141]"""]},
    {
        'Chinese': [
            """-  1 引言: [145, 32, 98]
-  2 预备知识: [176, 54, 23]
-  2.1 联邦学习简介: [121, 89, 187]
-  2.1.1 联邦学习中的术语: [142, 36, 75]
-  2.1.2 联邦学习的训练过程: [178, 47, 156]
-  2.2 联邦学习的分类和概念: [199, 68, 103]
-  2.2.1 数据划分: [57, 189, 112]
-  2.2.2 通信架构: [91, 161, 83]
-  3 隐私与安全: [134, 149, 66]
-  3.1 隐私保护联邦学习中的问题: [153, 48, 173]
-  3.1.1 重建攻击: [122, 194, 101]
-  3.2 隐私保护联邦学习的技术解决方案: [116, 77, 137]
-  3.2.1 噪声注入技术: [186, 53, 97]
-  3.2.2 匿名化技术: [182, 72, 168]
-  3.2.3 消息掩码: [139, 93, 155]
-  3.3 隐私保护联邦学习的挑战与未来方向: [104, 162, 200]
-  4 鲁棒性: [127, 95, 188]
-  4.1 鲁棒联邦学习中的问题: [163, 85, 174]
-  4.1.1 鲁棒联邦学习中的威胁模型: [192, 140, 79]
-  4.1.2 拜占庭问题: [176, 99, 184]
-  4.1.3 后门攻击: [132, 56, 197]
-  4.2 鲁棒联邦学习的技术解决方案: [154, 182, 70]
-  4.2.1 鲁棒聚合: [145, 88, 199]
-  4.2.2 恶意客户端检测: [175, 109, 141]
-  4.2.3 混合机制: [137, 180, 96]
-  4.3 鲁棒联邦学习的挑战与未来方向: [166, 133, 190]
-  5 公平性: [158, 50, 201]
-  5.1 公平感知联邦学习中的问题: [120, 148, 195]
-  5.1.1 不公平的客户端选择: [177, 82, 146]
-  5.1.2 不公平的模型优化: [169, 115, 172]
-  5.1.3 不公平的激励机制: [151, 86, 123]
-  5.1.4 有偏差的训练数据: [198, 170, 107]
-  5.2 公平感知联邦学习的技术解决方案: [118, 157, 102]
-  5.2.1 性能公平性: [129, 185, 144]
-  5.2.2 贡献公平性: [164, 128, 111]
-  5.2.3 属性公平性: [181, 167, 152]
-  5.3 公平感知联邦学习的挑战与未来方向: [159, 100, 193]
-  6 可解释性: [187, 106, 183]
-  6.1 事前可解释性: [171, 176, 125]
-  6.1.1 可解释的联邦学习过程: [165, 130, 119]
-  6.1.2 利用内在模型可解释性: [110, 160, 147]
-  6.2 事后可解释性: [143, 131, 124]
-  6.2.1 模型评估: [126, 105, 179]
-  6.3 可解释联邦学习的挑战与未来方向: [174, 150, 117]
-  7 结论: [155, 108, 138]""",
            """-  1 引言: [145, 37, 92]
-  1.1 挑战: [173, 56, 198]
-  1.2 相关综述: [163, 88, 149]
-  1.3 贡献: [187, 72, 135]
-  2 综述方法: [142, 104, 190]
-  3 符号和问题定义: [128, 177, 101]
-  4 输入分类: [166, 96, 182]
-  4.1 输入类型：模型的输入类型: [109, 181, 132]
-  4.1.1 用户 - 物品评分: [153, 75, 143]
-  4.1.2 用户 - 用户社交关系: [196, 113, 85]
-  4.1.3 附加特征: [189, 120, 157]
-  4.2 输入表示：模型内输入的表示: [155, 172, 108]
-  4.2.1 用户 - 用户/用户 - 物品图: [199, 138, 122]
-  4.2.2 用户 - 用户 - 物品图: [136, 111, 178]
-  4.2.3 属性图: [194, 98, 167]
-  4.2.4 多路图: [129, 185, 140]
-  4.2.5 用户 - 用户/用户 - 物品/物品 - 物品图: [176, 95, 158]
-  4.2.6 超图: [105, 130, 192]
-  4.2.7 去中心化图: [147, 184, 116]
-  5 架构分类: [102, 151, 195]
-  5.1 编码器: [139, 124, 177]
-  5.1.1 图卷积网络（GCN）: [110, 174, 134]
-  5.1.2 轻量级图卷积网络（LightGCN）: [170, 97, 188]
-  5.1.3 图注意力神经网络（GANN）: [127, 100, 144]
-  5.1.4 异构图神经网络（HetGNN）: [161, 82, 114]
-  5.1.5 图循环神经网络（GRNN）: [191, 87, 126]
-  5.1.6 超图神经网络（HyperGNN）: [200, 168, 106]
-  5.1.7 其他: [99, 164, 123]
-  5.2 解码器: [154, 180, 90]
-  5.2.1 点积: [117, 86, 146]
-  5.2.2 多层感知机（MLP）: [183, 79, 152]
-  5.3 损失函数: [197, 141, 133]
-  5.3.1 主要损失函数: [165, 76, 186]
-  5.3.2 辅助损失函数: [169, 119, 93]
-  5.4 模型复杂度: [115, 131, 193]
-  6 实验设置: [103, 160, 125]
-  6.1 基准数据集: [107, 156, 159]
-  6.1.1 与产品相关的数据集: [179, 150, 121]
-  6.1.2 与位置相关的数据集: [137, 71, 118]
-  6.1.3 与电影相关的数据集: [175, 94, 112]
-  6.2 评估指标: [171, 77, 148]
-  6.2.1 评分预测任务: [162, 89, 146]
-  6.2.2 前N推荐任务: [186, 191, 128]
-  6.3 实验结果: [99, 109, 199]
-  7 未来方向: [145, 113, 184]
-  7.1 基于图神经网络的社交推荐系统中的图增强: [108, 165, 197]
-  7.2 可信的基于图神经网络的社交推荐系统: [163, 153, 135]
-  7.3 异构性: [126, 180, 91]
-  7.4 效率和可扩展性: [192, 144, 114]
-  8 结论: [110, 122, 189]""",
            """-  1 引言: [145, 32, 91]
-  1.1 机器遗忘的动机: [178, 56, 203]
-  1.2 本综述的贡献: [189, 75, 167]
-  1.3 与现有机器遗忘综述的比较: [153, 88, 192]
-  2 预备知识: [131, 98, 204]
-  2.1 机器遗忘的定义: [143, 120, 186]
-  2.2 机器遗忘的目标: [173, 97, 134]
-  2.3 机器遗忘的期望特性: [195, 110, 162]
-  3 遗忘和验证机制分类: [184, 140, 125]
-  3.1 遗忘分类: [109, 181, 138]
-  3.1.1 数据重组: [156, 92, 190]
-  3.1.2 模型操作: [136, 108, 175]
-  3.2 验证机制: [151, 144, 122]
-  3.2.1 实证评估: [168, 113, 102]
-  3.2.2 理论计算: [185, 101, 154]
-  4 数据重组: [198, 137, 114]
-  4.1 基于数据混淆的数据重组: [171, 160, 193]
-  4.1.1 基于数据混淆的遗忘方案: [126, 99, 182]
-  4.1.2 基于数据混淆方案的可验证性: [133, 79, 199]
-  4.2 基于数据剪枝的数据重组: [163, 180, 103]
-  4.2.1 基于数据剪枝的遗忘方案: [119, 172, 145]
-  4.2.2 基于数据剪枝方案的可验证性: [200, 93, 187]
-  4.3 基于数据替换的数据重组: [177, 107, 124]
-  4.3.1 基于数据替换的遗忘方案: [194, 112, 157]
-  4.3.2 基于数据替换方案的可验证性: [182, 116, 169]
-  4.4 数据重组总结: [191, 130, 97]
-  5 模型操作: [166, 141, 127]
-  5.1 基于模型偏移的操作: [174, 121, 104]
-  5.1.1 基于模型偏移的遗忘方案: [152, 186, 195]
-  5.1.2 基于参数偏移方案的可验证性: [111, 123, 170]
-  5.2 基于模型剪枝的操作: [201, 146, 106]
-  5.2.1 基于模型剪枝的遗忘方案: [188, 117, 96]
-  5.2.2 基于模型剪枝方案的可验证性: [197, 147, 200]
-  5.3 基于模型替换的操作: [161, 139, 183]
-  5.3.1 基于模型替换的遗忘方案: [132, 109, 99]
-  5.3.2 基于模型替换方案的可验证性: [179, 135, 115]
-  5.4 模型操作总结: [176, 118, 126]
-  6 开放问题与未来方向: [105, 142, 155]
-  6.1 开放问题: [158, 100, 143]
-  6.1.1 遗忘解决方案的通用性: [192, 91, 137]
-  6.1.2 机器遗忘的安全性: [202, 150, 128]
-  6.1.3 机器遗忘的验证: [203, 110, 187]
-  6.1.4 机器遗忘的应用: [113, 185, 144]
-  6.2 未来方向: [149, 112, 198]
-  7 结论: [159, 164, 136]"""
        ],
        'English': [
            """- 1 Introduction: [145, 32, 98]
- 2 Preliminary knowledge: [176, 54, 23]
- 2.1 Introduction to federated learning: [121, 89, 187]
- 2.1.1 Terminology in federated learning: [142, 36, 75]
- 2.1.2 Training process of federated learning: [178, 47, 156]
- 2.2 Classification and concepts of federated learning: [199, 68, 103]
- 2.2.1 Data partitioning: [57, 189, 112]
- 2.2.2 Communication architecture: [91, 161, 83]
- 3 Privacy and security: [134, 149, 66]
- 3.1 Issues in privacy-preserving federated learning: [153, 48, 173]
- 3.1.1 Reconstruction attack: [122, 194, 101]
- 3.2 Technical solutions for privacy-preserving federated learning: [116, 77, 137]
- 3.2.1 Noise injection technology: [186, 53, 97]
- 3.2.2 Anonymization technology: [182, 72, 168]
- 3.2.3 Message masking: [139, 93, 155]
- 3.3 Challenges and future directions of privacy-preserving federated learning: [104, 162, 200]
- 4 Robustness: [127, 95, 188]
- 4.1 Problems in robust federated learning: [163, 85, 174]
- 4.1.1 Threat model in robust federated learning: [192, 140, 79]
- 4.1.2 Byzantine problem: [176, 99, 184]
- 4.1.3 Backdoor attack: [132, 56, 197]
- 4.2 Technical solutions for robust federated learning: [154, 182, 70]
- 4.2.1 Robust aggregation: [145, 88, 199]
- 4.2.2 Malicious client detection: [175, 109, 141]
- 4.2.3 Hybrid mechanism: [137, 180, 96]
- 4.3 Challenges and future directions of robust federated learning: [166, 133, 190]
- 5 Fairness: [158, 50, 201]
- 5.1 Issues in fairness-aware federated learning: [120, 148, 195]
- 5.1.1 Unfair client selection: [177, 82, 146]
- 5.1.2 Unfair model optimization: [169, 115, 172]
- 5.1.3 Unfair incentive mechanism: [151, 86, 123]
- 5.1.4 Biased training data: [198, 170, 107]
- 5.2 Technical solutions for fairness-aware federated learning: [118, 157, 102]
- 5.2.1 Performance fairness: [129, 185, 144]
- 5.2.2 Contribution fairness: [164, 128, 111]
- 5.2.3 Attribute fairness: [181, 167, 152]
- 5.3 Challenges and future directions of fairness-aware federated learning: [159, 100, 193]
- 6 Interpretability: [187, 106, 183]
- 6.1 Ex ante interpretability: [171, 176, 125]
- 6.1.1 Interpretable federated learning process: [165, 130, 119]
- 6.1.2 Leveraging intrinsic model interpretability: [110, 160, 147]
- 6.2 Ex post interpretability: [143, 131, 124]
- 6.2.1 Model evaluation: [126, 105, 179]
- 6.3 Challenges and future directions of interpretable federated learning: [174, 150, 117]
- 7 Conclusion: [155, 108, 138]""",
            """- 1 Introduction: [145, 37, 92]
- 1.1 Challenges: [173, 56, 198]
- 1.2 Related reviews: [163, 88, 149]
- 1.3 Contributions: [187, 72, 135]
- 2 Review methods: [142, 104, 190]
- 3 Notation and problem definition: [128, 177, 101]
- 4 Input classification: [166, 96, 182]
- 4.1 Input type: the input type of the model: [109, 181, 132]
- 4.1.1 User-item ratings: [153, 75, 143]
- 4.1.2 User-user social relationships: [196, 113, 85]
- 4.1.3 Additional features: [189, 120, 157]
- 4.2 Input representation: Representation of input within the model: [155, 172, 108]
- 4.2.1 User-user/user-item graph: [199, 138, 122]
- 4.2.2 User-user-item graph: [136, 111, 178]
- 4.2.3 Attribute graph: [194, 98, 167]
- 4.2.4 Multi-path graph: [129, 185, 140]
- 4.2.5 User-user/user-item/item-item graph: [176, 95, 158]
- 4.2.6 Hypergraph: [105, 130, 192]
- 4.2.7 Decentralized Graph: [147, 184, 116]
- 5 Architecture Classification: [102, 151, 195]
- 5.1 Encoder: [139, 124, 177]
- 5.1.1 Graph Convolutional Network (GCN): [110, 174, 134]
- 5.1.2 Lightweight Graph Convolutional Network (LightGCN): [170, 97, 188]
- 5.1.3 Graph Attention Neural Network (GANN): [127, 100, 144]
- 5.1.4 Heterogeneous Graph Neural Network (HetGNN): [161, 82, 114]
- 5.1.5 Graph Recurrent Neural Network (GRNN): [191, 87, 126]
- 5.1.6 HyperGNN: [200, 168, 106]
- 5.1.7 Others: [99, 164, 123]
- 5.2 Decoder: [154, 180, 90]
- 5.2.1 Dot product: [117, 86, 146]
- 5.2.2 Multilayer Perceptron (MLP): [183, 79, 152]
- 5.3 Loss function: [197, 141, 133]
- 5.3.1 Main loss function: [165, 76, 186]
- 5.3.2 Auxiliary loss function: [169, 119, 93]
- 5.4 Model complexity: [115, 131, 193]
- 6 Experimental settings: [103, 160, 125]
- 6.1 Benchmark datasets: [107, 156, 159]
- 6.1.1 Product-related datasets: [179, 150, 121]
- 6.1.2 Location-related datasets: [137, 71, 118]
- 6.1.3 Movie-related datasets: [175, 94, 112]
- 6.2 Evaluation metrics: [171, 77, 148]
- 6.2.1 Rating prediction task: [162, 89, 146]
- 6.2.2 Top-N recommendation task: [186, 191, 128]
- 6.3 Experimental results: [99, 109, 199]
- 7 Future directions: [145, 113, 184]
- 7.1 Graph Augmentation in Social Recommender Systems Based on Graph Neural Networks: [108, 165, 197]
- 7.2 Trustworthy Social Recommender Systems Based on Graph Neural Networks: [163, 153, 135]
- 7.3 Heterogeneity: [126, 180, 91]
- 7.4 Efficiency and Scalability: [192, 144, 114]
- 8 Conclusion: [110, 122, 189]""",
            """- 1 Introduction: [145, 32, 91]
- 1.1 Motivation for machine forgetting: [178, 56, 203]
- 1.2 Contributions of this review: [189, 75, 167]
- 1.3 Comparison with existing machine forgetting reviews: [153, 88, 192]
- 2 Preliminary knowledge: [131, 98, 204]
- 2.1 Definition of machine forgetting: [143, 120, 186]
- 2.2 Goals of machine forgetting: [173, 97, 134]
- 2.3 Desired properties of machine forgetting: [195, 110, 162]
- 3 Classification of forgetting and verification mechanisms: [184, 140, 125]
- 3.1 Classification of forgetting: [109, 181, 138]
- 3.1.1 Data Restructuring: [156, 92, 190]
- 3.1.2 Model Operation: [136, 108, 175]
- 3.2 Verification Mechanism: [151, 144, 122]
- 3.2.1 Empirical Evaluation: [168, 113, 102]
- 3.2.2 Theoretical Calculation: [185, 101, 154]
- 4 Data Restructuring: [198, 137, 114]
- 4.1 Data Restructuring Based on Data Obfuscation: [171, 160, 193]
- 4.1.1 Forgetting Scheme Based on Data Obfuscation: [126, 99, 182]
- 4.1.2 Verifiability of Data Obfuscation Scheme: [133, 79, 199]
- 4.2 Data Reorganization Based on Data Pruning: [163, 180, 103]
- 4.2.1 Forgetting Scheme Based on Data Pruning: [119, 172, 145]
- 4.2.2 Verifiability of Data Pruning Scheme: [200, 93, 187]
- 4.3 Data Reorganization Based on Data Replacement: [177, 107, 124]
- 4.3.1 Forgetting Scheme Based on Data Replacement: [194, 112, 157]
- 4.3.2 Verifiability of Data Replacement Scheme: [182, 116, 169]
- 4.4 Summary of Data Reorganization: [191, 130, 97]
- 5 Model Operation: [166, 141, 127]
- 5.1 Operations based on model shift: [174, 121, 104]
- 5.1.1 Forgetting scheme based on model shift: [152, 186, 195]
- 5.1.2 Verification of parameter shift scheme: [111, 123, 170]
- 5.2 Operations based on model pruning: [201, 146, 106]
- 5.2.1 Forgetting scheme based on model pruning: [188, 117, 96]
- 5.2.2 Verification of model pruning scheme: [197, 147, 200]
- 5.3 Operations based on model replacement: [161, 139, 183]
- 5.3.1 Forgetting scheme based on model replacement: [132, 109, 99]
- 5.3.2 Verification of model replacement scheme: [179, 135, 115]
- 5.4 Summary of model operation: [176, 118, 126]
- 6 Open issues and future directions: [105, 142, 155]
- 6.1 Open issues: [158, 100, 143]
- 6.1.1 Generalizability of forgetting solutions: [192, 91, 137]
- 6.1.2 Safety of machine forgetting: [202, 150, 128]
- 6.1.3 Verification of machine forgetting: [203, 110, 187]
- 6.1.4 Applications of machine forgetting: [113, 185, 144]
- 6.2 Future directions: [149, 112, 198]
- 7 Conclusion: [159, 164, 136]"""]
    }
]

generate_description_samples = {
    "Chinese": [
        '它介绍了少样本学习（FSL）的研究背景、动机、挑战及本文主要贡献，包括对相关概念的区分、基于先验知识的分类、对近期论文的调研及未来研究方向的探讨。',
        '它讲述了少样本目标检测的相关工作及在 MS COCO 数据集上的成果。',
        '它对超参数调整问题进行概述，并介绍了相关优化方法。',
        '它对不同的元学习方法和 AutoML 系统进行了比较分析。',
        '它说明 CVI 选择在设计 AutoML 聚类管道中的重要性及面临的挑战。',
        '它介绍了自动化机器学习（AutoML）在聚类中的应用背景、动机、贡献及文章结构。',
        '它介绍少样本学习预训练阶段，使用多种网络在大规模数据集上进行有监督或无监督训练，阐述了 Transformer 及其相关模型的特点与作用，以及预训练对减少类内差异和提取语义特征的重要性。',
        '它介绍多模态学习在少样本学习中的应用及优势，鼓励尝试更多模态融合学习，提出量化多信息融合中各类信息重要性的研究方向。',
        '它总结 AutoML 聚类仍存在挑战，需进一步探索和发展以解决相关问题。',
        '它阐述了超图嵌入方法的原理及典型算法（如 MGCN）。',
        '它概述了动态 GNN（DGNN）的原理及典型算法（如 EvolveGCN、DyGNN 等）。'],
    "English": [
"It provides an overview of the research background, motivation, challenges and main contributions of Few-Shot Learning (FSL), including the distinction of related concepts, classification based on prior knowledge, investigation of recent papers and discussion on future research directions. ",
"It elaborates on the relevant works on few-shot object detection and the corresponding achievements on the MS COCO dataset.",
"It provides an overview of the hyperparameter tuning issue and introduces related optimization methods.",
"It conducted a comparative analysis of various meta-learning methods and AutoML systems.",
"It highlights the significance of CVI in the design of AutoML clustering pipelines and the challenges it faces.",
"It provides an introduction to the application context, motivation, contribution and article structure of AutoML in clustering.",
"It introduces the pre-training stage of few-shot learning, where multiple networks are trained on large-scale datasets through supervised or unsupervised learning. It elaborates on the characteristics and functions of Transformer and its related models, as well as the significance of pre-training in reducing intra-class differences and extracting semantic features. ",
"It introduces the application and advantages of multimodal learning in few-shot learning, encourages the exploration of more modal fusion learning, and proposes a research direction for quantifying the importance of various types of information in multi-information fusion.",
"It concludes that there are still challenges in AutoML clustering and further exploration and development are needed to address the related issues.",
"It elaborates on the principles and typical algorithms (such as MGCN) of the hypergraph embedding method.",
"It provides an overview of the principles and typical algorithms of Dynamic Graph Neural Network (DGNN), such as EvolveGCN and DyGNN, etc."
]
}
# todo 需要删掉
keyinfo_classify_section_prompts = {
    "Chinese": """
    请根据以下综述大纲和章节描述，将提供的参考文献关键信息文本块划分到最相关的叶子章节。每个文本块最多可以划分到两个叶子章节，并确保划分的依据与章节描述高度相关。

    **综述大纲及章节描述：**
    \"\"\"
    %s  
    \"\"\"
    
    **参考文献关键信息文本块：**
    \"\"\"
    %s
    \"\"\"
    
    **参考样例输出：**
    - 样例输出1
    {
    '65fc055c13fb2c6cf6df20e9_0': ['#### 3.1.1 强化学习', '#### 3.3.2 基于模型和数据驱动的结合'],
    '65fc055c13fb2c6cf6df20e9_1': ['### 1.3 研究意义', '### 4.3 3DBench'],
    '65fc055c13fb2c6cf6df20e9_2': ['### 5.3 未来研究方向'],
    ...,
    '61a98aff5244ab9dcb955ef5_8': ['### 3.3.3 多目标优化', '### 5.1 技术挑战']
    }
    - 样例输出2
    {
    '684c0d5c16fb2c4936df20e9_0': ['#### 3.1.1 强化学习', '### 4.3 3DBench']
    '684c0d5c16fb2c4936df20e9_1': ['### 5.1 技术挑战'],
    ...
    }
    
    请为每个文本块指定最相关的叶子章节列表。如果某个文本块适用于两个叶子章节，请用逗号分隔两个章节编号。
    """,

    "English": """Please analyze the content of each chunk (in the form of extracted statements) from every research paper and identify which leaf nodes (nodes without child nodes) in the survey outline it corresponds to. The goal is to provide a mapping for each statement, listing the relevant leaf nodes for each one, for use during the drafting process. Each mapping should correspond to at most two leaf nodes.
            **Input details:**
                1. Survey Outline:
                \"\"\"
                %s
                \"\"\"
                2.claim Information from Research Papers chunk:
                \"\"\"
                %s
                \"\"\"
            **Reference sample outputs:**
                '454847912576693862':['#### 3.1.1 Reinforcement Learning','#### 3.3.2 Combination of Model-Based and Data-Driven Approaches'],
                '454847396319978506':['### 1.3 Research Significance','### 4.3 3DBench'],
                '454847923072940022':['#### 3.1.1 Reinforcement Learning','### 5.3 Future Research Directions'],  
                '454919043627627476':['### 3.3.3 Multi-Objective Optimization','### 5.1 Technical Challenges']  
             **Task Requirements:**
             1.For each chunk from every research paper, analyze its main content and identify which leaf nodes(without child nodes) in the survey outline it corresponds to.
             2.Provide a mapping for each chunk, listing the relevant leaf nodes(without child nodes) for each one.
             3.Each Chunk_id will only be assigned to at most two leaf nodes.
     """
}
# todo 需要删掉
evaluate_correlation_prompt = {
    "Chinese": """
    **任务描述：**
作为一位计算机领域的专家，你的任务是依据提供的文章标题和摘要，评估该文章是否适合作为特定主题文献综述的参考文献。请根据以下打分细则进行十分制评分，并提供详细的评分理由。

**特定主题：** %s

打分细则（仅考虑相关性）：

高度相关（8-10分）

10分： 文章主题与特定主题完全契合，直接讨论或研究特定主题的核心内容。
9分： 文章主题与特定主题非常相关，涵盖了特定主题的主要方面。
8分： 文章主题与特定主题高度相关，涉及特定主题的关键部分。
中度相关（4-7分）

7分： 文章主题与特定主题相关，讨论了特定主题的某些重要方面。
6分： 文章主题与特定主题有一定关联，涉及特定主题的相关技术或方法。
5分： 文章主题与特定主题有一定联系，但主要讨论的是相关领域的其他内容。
4分： 文章主题与特定主题有一定关联，但在主要内容上有所偏离。
低度相关（1-3分）

3分： 文章主题与特定主题关联性较弱，仅在某些细节上有所涉及。
2分： 文章主题与特定主题关联性很弱，主要内容与特定主题无关。
1分： 文章主题与特定主题基本无关，仅有个别词汇或概念与特定主题相关。
不相关（0分）

0分： 文章主题与特定主题完全无关。
**评分示例：**

示例1：
input:
{'title': "On the Planning Abilities of Large Language Models - A Critical Investigation.", 
 'abstract': \"\"\"# Abstract Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) how good LLMs are by themselves in generating and validating simple plans in commonsense planning tasks (of the type that humans are generally quite good at) and (2) how good LLMs are in being a source of heuristic guidance for other agents–either AI planners or human planners–in their planning tasks. To investigate these questions in a systematic rather than anecdotal manner, we start by developing a benchmark suite based on the kinds of domains employed in the International Planning Competition. On this benchmark, we evaluate LLMs in three modes: autonomous, heuristic and human-in-the-loop. Our results show that LLM’s ability to autonomously generate executable plans is quite meager, averaging only about 3%% success rate. The heuristic and human-in-the-loop modes show slightly more promise. In addition to these results, we also make our benchmark and evaluation tools available to support investigations by the research community.\"\"\"}
output:
{'score': 10, 
 'explanation': \"\"\"文章标题直接提及大语言模型（LLMs）的规划能力，摘要明确阐述旨在研究 LLMs 在生成和验证简单计划方面的能力，以及作为启发式指导来源对其他主体规划任务的作用。文中还开发了基准测试套件，并对 LLMs 在三种模式下进行评估，核心内容完全围绕大模型的规划能力展开，与特定主题 “大模型的规划能力” 完全契合，故评 10 分。\"\"\"}

示例2：
input:
{'title': "Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques", 
 'abstract': \"\"\"This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.\"\"\"}
output:
{'score': 0,
 'explanation': \"\"\"文章标题及摘要主要围绕多语言模型中偏差校正技术的跨语言迁移展开，研究的是不同语言环境下偏差校正技术的可行性、效果以及哪种技术最优等内容，完全未涉及大模型的规划能力相关话题，与特定主题 “大模型的规划能力” 完全无关，所以根据打分细则应评为 0 分。\"\"\"}
 
示例3：
input:
{'title': "Diffusion-based Generation, Optimization, and Planning in 3D Scenes.", 
 'abstract': \"\"\"# Abstract We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior work, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative sampling strategy, SceneDiffuser jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion. Such a design alleviates the discrepancies among different modules and the posterior collapse of previous scene-conditioned generative models. We evaluate SceneDiffuser on various 3D scene understanding tasks, including human pose and motion generation, dexterous grasp generation, path planning for 3D navigation, and motion planning for robot arms. The results show significant improvements compared with previous models, demonstrating the tremendous potential of SceneDiffuser for the broad community of 3D scene understanding.\"\"\"'}
output:
{'score': 7,
 'explanation': 
""",
    "English": """
     **Task Description：**
As an expert in the field of computer science, your task is to evaluate whether the provided article title and abstract are suitable as a reference for a literature review on a specific topic. Please score the article on a ten-point scale according to the following scoring criteria and provide a detailed explanation for your score.
**Specific Topic：** %s

Scoring Criteria (Only considering relevance)：

Highly Relevant (8-10 points)

10 points： The article’s topic aligns perfectly with the specific topic, directly discussing or researching the core aspects of the specific topic.
9 points： The article’s topic is highly related to the specific topic, covering most major aspects of the specific topic.
8 points：The article’s topic is closely related to the specific topic, addressing key components of the specific topic.
Moderately Relevant (4-7 points)

7 points： The article’s topic is relevant to the specific topic, discussing some important aspects of the specific topic.
6 points： he article’s topic has some relation to the specific topic, involving related techniques or methods.
5 points： The article’s topic has some connection to the specific topic, but mainly discusses other content from related fields.
4 points： The article’s topic is somewhat related to the specific topic but deviates significantly in the main content.
Low Relevance (1-3 points)

3 points： The article’s topic has weak relevance to the specific topic, only touching on certain details.
2 points： The article’s topic has very weak relevance to the specific topic, with the main content unrelated to the specific topic.
1 points： The article’s topic is largely unrelated to the specific topic, with only a few terms or concepts loosely connected to it.
Not Relevant (0 points)

0分： The article’s topic is completely unrelated to the specific topic.
**Score Example：**

Sample 1：
input:
{'title': "On the Planning Abilities of Large Language Models - A Critical Investigation.", 
 'abstract': \"\"\"# Abstract Intrigued by the claims of emergent reasoning capabilities in LLMs trained on general web corpora, in this paper, we set out to investigate their planning capabilities. We aim to evaluate (1) how good LLMs are by themselves in generating and validating simple plans in commonsense planning tasks (of the type that humans are generally quite good at) and (2) how good LLMs are in being a source of heuristic guidance for other agents–either AI planners or human planners–in their planning tasks. To investigate these questions in a systematic rather than anecdotal manner, we start by developing a benchmark suite based on the kinds of domains employed in the International Planning Competition. On this benchmark, we evaluate LLMs in three modes: autonomous, heuristic and human-in-the-loop. Our results show that LLM’s ability to autonomously generate executable plans is quite meager, averaging only about 3%% success rate. The heuristic and human-in-the-loop modes show slightly more promise. In addition to these results, we also make our benchmark and evaluation tools available to support investigations by the research community.\"\"\"}
output:
{'score': 10, 
 'explanation': \"\"\"The article title directly mentions the planning capabilities of large language models (LLMs), and the abstract clearly states that the aim is to study LLMs' ability to generate and verify simple plans, as well as their role as a heuristic guide for other planning tasks. The paper also develops a benchmarking suite and evaluates LLMs in three modes. The core content is entirely focused on the planning capabilities of large models, which aligns perfectly with the specific topic "planning capabilities of large models," hence a score of 10 points.\"\"\"}

Sample 2：
input:
{'title': "Investigating Bias in Multilingual Language Models: Cross-Lingual Transfer of Debiasing Techniques", 
 'abstract': \"\"\"This paper investigates the transferability of debiasing techniques across different languages within multilingual models. We examine the applicability of these techniques in English, French, German, and Dutch. Using multilingual BERT (mBERT), we demonstrate that cross-lingual transfer of debiasing techniques is not only feasible but also yields promising results. Surprisingly, our findings reveal no performance disadvantages when applying these techniques to non-English languages. Using translations of the CrowS-Pairs dataset, our analysis identifies SentenceDebias as the best technique across different languages, reducing bias in mBERT by an average of 13%%. We also find that debiasing techniques with additional pretraining exhibit enhanced cross-lingual effectiveness for the languages included in the analyses, particularly in lower-resource languages. These novel insights contribute to a deeper understanding of bias mitigation in multilingual language models and provide practical guidance for debiasing techniques in different language contexts.\"\"\"}
output:
{'score': 0,
 'explanation': \"\"\"The article title and abstract primarily focus on the cross-linguistic transfer of bias correction techniques in multilingual models, investigating the feasibility, effectiveness, and optimal techniques in different language environments. It does not address the topic of planning capabilities of large models, which is entirely unrelated to the specific topic "planning capabilities of large models." Therefore, according to the scoring criteria, it should be rated 0 points.\"\"\"}
 
Sample 3：
input:
{'title': "Diffusion-based Generation, Optimization, and Planning in 3D Scenes.", 
 'abstract': \"\"\"# Abstract We introduce SceneDiffuser, a conditional generative model for 3D scene understanding. SceneDiffuser provides a unified model for solving scene-conditioned generation, optimization, and planning. In contrast to prior work, SceneDiffuser is intrinsically scene-aware, physics-based, and goal-oriented. With an iterative sampling strategy, SceneDiffuser jointly formulates the scene-aware generation, physics-based optimization, and goal-oriented planning via a diffusion-based denoising process in a fully differentiable fashion. Such a design alleviates the discrepancies among different modules and the posterior collapse of previous scene-conditioned generative models. We evaluate SceneDiffuser on various 3D scene understanding tasks, including human pose and motion generation, dexterous grasp generation, path planning for 3D navigation, and motion planning for robot arms. The results show significant improvements compared with previous models, demonstrating the tremendous potential of SceneDiffuser for the broad community of 3D scene understanding.\"\"\"'}
output:
{'score': 7,
 'explanation': 
    """
}

cut_serial_prompt = {
    "Chinese": """"
    请从以下大纲中移除所有摘要性章节和总结性章节的内容，保留其他所有章节及其描述。摘要性章节通常以‘摘要’为标题，总结性章节通常以‘结论’或‘总结’为标题。确保只删除这些特定章节，不修改其他任何内容。    
    **具体操作步骤：**
    
    1. 识别摘要性章节和总结性章节：摘要性章节通常以“摘要”为标题。总结性章节通常以“结论”或“总结”为标题。
    2. 删除这些章节：从大纲中移除这些特定章节及其描述。
    3. 保留其他章节：确保其他所有章节及其描述保持不变。
    **输入大纲：**
    %s
    
    不要输出分析过程，直接输出结果
    """,
    "English": """
    Please remove all abstract and conclusion chapters from the following outline, while keeping all other chapters and their descriptions. Abstract chapters usually have the title 'Abstract,' and conclusion chapters typically have the titles 'Conclusion' or 'Summary.' Ensure that only these specific chapters are removed, and no other content is modified. 
    **Specific steps：**
    
    1. Identify abstract and conclusion chapters ：Abstract chapters usually have the title "Abstract." Conclusion chapters typically have the titles "Conclusion" or "Summary".
    2. Remove these chapters ：Remove these specific chapters and their descriptions from the outline.
    3. Keep other chapter：nsure that all other chapters and their descriptions remain unchanged.
    **Input Outline：**
    %s
    
    Do not output the analysis process, just provide the result.
    """

}

youhua_claims_number = {
    "Chinese": """
    请根据下面的内容，来判定该篇文章被引用的声明集中：哪一个序号的声明，最可以被支撑声明集的chunk内容所支撑（支撑的意思是：当你提到一个结论性的声明时，你需要在参考文献中引用相关的文献来源，以此来表明你的声明是基于已有的研究成果或广泛的学术共识）
**注意**
    1、要返回声明的原文表述，不要只返回到序号，其他的内容都不需要，为什么是这个声明也不需要
    2、若是该篇文章被引用的声明集中只有一个声明的话，那么直接认为本chunk的内容可以支撑该声明，返回该声明即可
    3、若是该篇文章被引用的声明集中不止一个声明，但是支撑声明集的chunk内容无法支撑任何一个声明，那么这个时候我们就默认返回该篇文章被引用的声明集中的第一个声明
    4、若是支撑声明集的chunk内容为空，那么默认返回该篇文章被引用的声明集中的第一个声明
    5、保证处理的结果返回该篇文章被引用的声明集中的一个声明
**文章标题**
%s
**该篇文章被引用的声明集** 
%s
**支撑声明集的chunk内容**
%s
**结果返回**
只返回支撑声明集的chunk内容最能支撑的那个声明
    """,
    "English": """
    Please determine which numbered statement in the article is best supported by the content of the cited references. (To be “supported” means that when you mention a conclusive statement, you need to cite relevant sources in the references to indicate that your statement is based on existing research findings or a broad academic consensus.)
    **Note**
    1、Return the original statement, not just the serial number. Do not return any other content, and there is no need to explain why it is this statement.
    2、If there is only one statement in the cited statement set of this article, then directly consider that the content of this chunk can support this statement, and return this statement.
    3、If there are more than one statement in the cited statement set of this article, but the chunk content supporting the statement set cannot support any of the statements, then in this case, we will default to returning the first statement in the cited statement set of this article.
    4、If the chunk content supporting the statement set is empty, then default to returning the first statement in the cited statement set of this article.
    5、Ensure that the processing result returns one statement from the cited statement set of this article.
    **Article Title**
    %s
    **Cited Statement Set of This Article**
    %s
    **Chunk Content Supporting the Statement Set**
    %s
    **Result Return**
    When returning the result, only return the statement that this chunk can support the most
    """
}
